/* Copyright 2015 Google Inc. 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/op.h"

namespace tensorflow {

// --------------------------------------------------------------------------

REGISTER_OP("DynamicPartition")
    .Input("data: T")
    .Input("partitions: int32")
    .Output("outputs: num_partitions * T")
    .Attr("num_partitions: int")
    .Attr("T: type")
    .Doc(R"doc(
Partitions `data` into `num_partitions` tensors using indices from `partitions`.

For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
are placed in `outputs[i]` in lexicographic order of `js`, and the first
dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
In detail,

    outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]

    outputs[i] = pack([data[js, ...] for js if partitions[js] == i])

`data.shape` must start with `partitions.shape`.

For example:

    # Scalar partitions
    partitions = 1
    num_partitions = 2
    data = [10, 20]
    outputs[0] = []  # Empty with shape [0, 2]
    outputs[1] = [[10, 20]]

    # Vector partitions
    partitions = [0, 0, 1, 1, 0]
    num_partitions = 2
    data = [10, 20, 30, 40, 50]
    outputs[0] = [10, 20, 50]
    outputs[1] = [30, 40]

<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../images/DynamicPartition.png" alt>
</div>

partitions: Any shape.  Indices in the range `[0, num_partitions)`.
num_partitions: The number of partitions to output.
)doc");

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 2")
    .Attr("T : type")
    .Doc(R"doc(
Interleave the values from the `data` tensors into a single tensor.

Builds a merged tensor such that

    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]

For example, if each `indices[m]` is scalar or vector, we have

    # Scalar indices
    merged[indices[m], ...] = data[m][...]

    # Vector indices
    merged[indices[m][i], ...] = data[m][i, ...]

Each `data[i].shape` must start with the corresponding `indices[i].shape`,
and the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we
must have `data[i].shape = indices[i].shape + constant`.  In terms of this
`constant`, the output shape is

    merged.shape = [max(indices)] + constant

Values are merged in order, so if an index appears in both `indices[m][i]` and
`indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the
merged result.

For example:

    indices[0] = 6
    indices[1] = [4, 1]
    indices[2] = [[5, 2], [0, 3]]
    data[0] = [61, 62]
    data[1] = [[41, 42], [11, 12]]
    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
              [51, 52], [61, 62]]

<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../images/DynamicStitch.png" alt>
</div>
)doc");

// --------------------------------------------------------------------------

REGISTER_OP("RandomShuffleQueue")
    .Output("handle: Ref(string)")
    .Attr("component_types: list(type) >= 1")
    .Attr("shapes: list(shape) >= 0 = []")
    .Attr("capacity: int = -1")
    .Attr("min_after_dequeue: int = 0")
    .Attr("seed: int = 0")
    .Attr("seed2: int = 0")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .SetIsStateful()
    .Doc(R"doc(
A queue that randomizes the order of elements.

handle: The handle to the queue.
component_types: The type of each component in a value.
shapes: The shape of each component in a value. The length of this attr must
  be either 0 or the same as the length of component_types. If the length of
  this attr is 0, the shapes of queue elements are not constrained, and
  only one element may be dequeued at a time.
capacity: The upper bound on the number of elements in this queue.
  Negative numbers mean no limit.
min_after_dequeue: Dequeue will block unless there would be this
  many elements after the dequeue or the queue is closed. This
  ensures a minimum level of mixing of elements.
seed: If either seed or seed2 is set to be non-zero, the random number
  generator is seeded by the given seed.  Otherwise, a random seed is used.
seed2: A second seed to avoid seed collision.
container: If non-empty, this queue is placed in the given container.
        Otherwise, a default container is used.
shared_name: If non-empty, this queue will be shared under the given name
  across multiple sessions.
)doc");

REGISTER_OP("FIFOQueue")
    .Output("handle: Ref(string)")
    .Attr("component_types: list(type) >= 1")
    .Attr("shapes: list(shape) >= 0 = []")
    .Attr("capacity: int = -1")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .SetIsStateful()
    .Doc(R"doc(
A queue that produces elements in first-in first-out order.

handle: The handle to the queue.
component_types: The type of each component in a value.
shapes: The shape of each component in a value. The length of this attr must
  be either 0 or the same as the length of component_types. If the length of
  this attr is 0, the shapes of queue elements are not constrained, and
  only one element may be dequeued at a time.
capacity: The upper bound on the number of elements in this queue.
  Negative numbers mean no limit.
container: If non-empty, this queue is placed in the given container.
        Otherwise, a default container is used.
shared_name: If non-empty, this queue will be shared under the given name
  across multiple sessions.
)doc");

REGISTER_OP("PaddingFIFOQueue")
    .Output("handle: Ref(string)")
    .Attr("component_types: list(type) >= 1")
    .Attr("shapes: list(shape) >= 0 = []")
    .Attr("capacity: int = -1")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .SetIsStateful()
    .Doc(R"doc(
A queue that produces elements in first-in first-out order.

Variable-size shapes are allowed by setting the corresponding shape dimensions
to 0 in the shape attr.  In this case DequeueMany will pad up to the maximum
size of any given element in the minibatch.  See below for details.

handle: The handle to the queue.
component_types: The type of each component in a value.
shapes: The shape of each component in a value. The length of this attr must
  be either 0 or the same as the length of component_types.
  Shapes of fixed rank but variable size are allowed by setting
  any shape dimension to -1.  In this case, the inputs' shape may vary along
  the given dimension, and DequeueMany will pad the given dimension with
  zeros up to the maximum shape of all elements in the given batch.
  If the length of this attr is 0, different queue elements may have
  different ranks and shapes, but only one element may be dequeued at a time.
capacity: The upper bound on the number of elements in this queue.
  Negative numbers mean no limit.
container: If non-empty, this queue is placed in the given container.
  Otherwise, a default container is used.
shared_name: If non-empty, this queue will be shared under the given name
  across multiple sessions.
)doc");

REGISTER_OP("QueueEnqueue")
    .Input("handle: Ref(string)")
    .Input("components: Tcomponents")
    .Attr("Tcomponents: list(type) >= 1")
    .Attr("timeout_ms: int = -1")
    .Doc(R"doc(
Enqueues a tuple of one or more tensors in the given queue.

The components input has k elements, which correspond to the components of
tuples stored in the given queue.

N.B. If the queue is full, this operation will block until the given
element has been enqueued (or 'timeout_ms' elapses, if specified).

handle: The handle to a queue.
components: One or more tensors from which the enqueued tensors should be taken.
timeout_ms: If the queue is full, this operation will block for up to
  timeout_ms milliseconds.
  Note: This option is not supported yet.
)doc");

REGISTER_OP("QueueEnqueueMany")
    .Input("handle: Ref(string)")
    .Input("components: Tcomponents")
    .Attr("Tcomponents: list(type) >= 1")
    .Attr("timeout_ms: int = -1")
    .Doc(R"doc(
Enqueues zero or more tuples of one or more tensors in the given queue.

This operation slices each component tensor along the 0th dimension to
make multiple queue elements. All of the tuple components must have the
same size in the 0th dimension.

The components input has k elements, which correspond to the components of
tuples stored in the given queue.

N.B. If the queue is full, this operation will block until the given
elements have been enqueued (or 'timeout_ms' elapses, if specified).

handle: The handle to a queue.
components: One or more tensors from which the enqueued tensors should
  be taken.
timeout_ms: If the queue is too full, this operation will block for up
  to timeout_ms milliseconds.
  Note: This option is not supported yet.
)doc");

REGISTER_OP("QueueDequeue")
    .Input("handle: Ref(string)")
    .Output("components: component_types")
    .Attr("component_types: list(type) >= 1")
    .Attr("timeout_ms: int = -1")
    .Doc(R"doc(
Dequeues a tuple of one or more tensors from the given queue.

This operation has k outputs, where k is the number of components
in the tuples stored in the given queue, and output i is the ith
component of the dequeued tuple.

N.B. If the queue is empty, this operation will block until an element
has been dequeued (or 'timeout_ms' elapses, if specified).

handle: The handle to a queue.
components: One or more tensors that were dequeued as a tuple.
component_types: The type of each component in a tuple.
timeout_ms: If the queue is empty, this operation will block for up to
  timeout_ms milliseconds.
  Note: This option is not supported yet.
)doc");

REGISTER_OP("QueueDequeueMany")
    .Input("handle: Ref(string)")
    .Input("n: int32")
    .Output("components: component_types")
    .Attr("component_types: list(type) >= 1")
    .Attr("timeout_ms: int = -1")
    .Doc(R"doc(
Dequeues n tuples of one or more tensors from the given queue.

This operation concatenates queue-element component tensors along the
0th dimension to make a single component tensor.  All of the components
in the dequeued tuple will have size n in the 0th dimension.

This operation has k outputs, where k is the number of components in
the tuples stored in the given queue, and output i is the ith
component of the dequeued tuple.

N.B. If the queue is empty, this operation will block until n elements
have been dequeued (or 'timeout_ms' elapses, if specified).

handle: The handle to a queue.
n: The number of tuples to dequeue.
components: One or more tensors that were dequeued as a tuple.
component_types: The type of each component in a tuple.
timeout_ms: If the queue has fewer than n elements, this operation
  will block for up to timeout_ms milliseconds.
  Note: This option is not supported yet.
)doc");

REGISTER_OP("QueueClose")
    .Input("handle: Ref(string)")
    .Attr("cancel_pending_enqueues: bool = false")
    .Doc(R"doc(
Closes the given queue.

This operation signals that no more elements will be enqueued in the
given queue. Subsequent Enqueue(Many) operations will fail.
Subsequent Dequeue(Many) operations will continue to succeed if
sufficient elements remain in the queue. Subsequent Dequeue(Many)
operations that would block will fail immediately.

handle: The handle to a queue.
cancel_pending_enqueues: If true, all pending enqueue requests that are
  blocked on the given queue will be cancelled.
)doc");

REGISTER_OP("QueueSize")
    .Input("handle: Ref(string)")
    .Output("size: int32")
    .Doc(R"doc(
Computes the number of elements in the given queue.

handle: The handle to a queue.
size: The number of elements in the given queue.
)doc");

// --------------------------------------------------------------------------

REGISTER_OP("Stack")
    .Output("handle: Ref(string)")
    .Attr("elem_type: type")
    .Attr("stack_name: string = ''")
    .SetIsStateful()
    .Doc(R"doc(
A stack that produces elements in first-in last-out order.

handle: The handle to the stack.
elem_type: The type of the elements on the stack.
stack_name: Overrides the name used for the temporary stack resource. Default
value is the name of the 'Stack' op (which is guaranteed unique).
)doc");

REGISTER_OP("StackPush")
    .Input("handle: Ref(string)")
    .Input("elem: T")
    .Output("output: T")
    .Attr("T: type")
    .Attr("swap_memory: bool = false")
    .Doc(R"doc(
Push an element onto the stack.

handle: The handle to a stack.
elem: The tensor to be pushed onto the stack.
output: The same tensor as the input 'elem'.
swap_memory: Swap `elem` to CPU. Default to false.
)doc");

REGISTER_OP("StackPop")
    .Input("handle: Ref(string)")
    .Output("elem: elem_type")
    .Attr("elem_type: type")
    .Doc(R"doc(
Pop the element at the top of the stack.

handle: The handle to a stack.
elem: The tensor that is popped from the top of the stack.
elem_type: The type of the elem that is popped.
)doc");

REGISTER_OP("StackClose")
    .Input("handle: Ref(string)")
    .Doc(R"doc(
Delete the stack from its resource container.

handle: The handle to a stack.
)doc");

// --------------------------------------------------------------------------

REGISTER_OP("TensorArray")
    .Input("size: int32")
    .Attr("dtype: type")
    .Attr("dynamic_size: bool = false")
    .Attr("clear_after_read: bool = true")
    .Attr("tensor_array_name: string = ''")
    .Output("handle: Ref(string)")
    .SetIsStateful()
    .Doc(R"doc(
An array of Tensors of given size, with data written via Write and read
via Read or Pack.

handle: The handle to the TensorArray.
size: The size of the array.
dtype: The type of the elements on the tensor_array.
dynamic_size: A boolean that determines whether writes to the TensorArray
  are allowed to grow the size.  By default, this is not allowed.
clear_after_read: If true (default), Tensors in the TensorArray are cleared
  after being read.  This disables multiple read semantics but allows early
  release of memory.
tensor_array_name: Overrides the name used for the temporary tensor_array
  resource. Default value is the name of the 'TensorArray' op (which
  is guaranteed unique).
)doc");

REGISTER_OP("TensorArrayGrad")
    .Input("handle: Ref(string)")
    .Input("flow_in: float")
    .Output("grad_handle: Ref(string)")
    .Attr("source: string")
    .SetIsStateful()
    .Doc(R"doc(
Creates a TensorArray for storing the gradients of values in the given handle.

If the given TensorArray gradient already exists, returns a reference to it.

Locks the size of the original TensorArray by disabling its dynamic size flag.

**A note about the input flow_in:**

The handle flow_in forces the execution of the gradient lookup to occur
only after certain other operations have occurred.  For example, when
the forward TensorArray is dynamically sized, writes to this TensorArray
may resize the object.  The gradient TensorArray is statically sized based
on the size of the forward TensorArray when this operation executes.
Furthermore, the size of the forward TensorArray is frozen by this call.
As a result, the flow is used to ensure that the call to generate the gradient
TensorArray only happens after all writes are executed.

In terms of e.g. python TensorArray sugar wrappers when using dynamically sized
TensorArrays:  Gradients should only be called on read operations that have
themselves been chained via flow to occur only after all writes have executed.
That way the final size of the forward TensorArray is known when this operation
is called.

**A note about the source attribute:**

TensorArray gradient calls use an accumulator TensorArray object.  If
multiple gradients are calculated and run in the same session, the multiple
gradient nodes may accidentally flow throuth the same accumulator TensorArray.
This double counts and generally breaks the TensorArray gradient flow.

The solution is to identify which gradient call this particular
TensorArray gradient is being called in.  This is performed by identifying
a unique string (e.g. "gradients", "gradients_1", ...) from the input
gradient Tensor's name.  This string is used as a suffix when creating
the TensorArray gradient object here (the attribute `source`).

The attribute `source` is added as a suffix to the forward TensorArray's
name when performing the creation / lookup, so that each separate gradient
calculation gets its own TensorArray accumulator.

handle: The handle to the forward TensorArray.
flow_in: A float scalar that enforces proper chaining of operations.
source: The gradient source string, used to decide which gradient TensorArray
  to return.
)doc");

REGISTER_OP("TensorArrayWrite")
    .Input("handle: Ref(string)")
    .Input("index: int32")
    .Input("value: T")
    .Input("flow_in: float")
    .Output("flow_out: float")
    .Attr("T: type")
    .Doc(R"doc(
Push an element onto the tensor_array.

handle: The handle to a TensorArray.
index: The position to write to inside the TensorArray.
value: The tensor to write to the TensorArray.
flow_in: A float scalar that enforces proper chaining of operations.
flow_out: A float scalar that enforces proper chaining of operations.
)doc");

REGISTER_OP("TensorArrayRead")
    .Input("handle: Ref(string)")
    .Input("index: int32")
    .Input("flow_in: float")
    .Output("value: dtype")
    .Attr("dtype: type")
    .Doc(R"doc(
Read an element from the TensorArray into output `value`.

handle: The handle to a TensorArray.
dtype: The type of the elem that is returned.
flow_in: A float scalar that enforces proper chaining of operations.
value: The tensor that is read from the TensorArray.
)doc");

REGISTER_OP("TensorArrayPack")
    .Input("handle: Ref(string)")
    .Input("flow_in: float")
    .Output("value: dtype")
    .Attr("dtype: type")
    .Doc(R"doc(
Pack the elements from the TensorArray into output `value`.

All elements must have the same shape.

handle: The handle to a TensorArray.
dtype: The type of the elem that is returned.
flow_in: A float scalar that enforces proper chaining of operations.
value: All of the elements in the TensorArray, concatenated along a new
  axis (the new dimension 0).
)doc");

REGISTER_OP("TensorArrayUnpack")
    .Input("handle: Ref(string)")
    .Input("value: T")
    .Input("flow_in: float")
    .Output("flow_out: float")
    .Attr("T: type")
    .Doc(R"doc(
Unpack the data from the input value into TensorArray elements.

handle: The handle to a TensorArray.
value: The concatenated tensor to write to the TensorArray.
flow_in: A float scalar that enforces proper chaining of operations.
flow_out: A float scalar that enforces proper chaining of operations.
)doc");

REGISTER_OP("TensorArrayConcat")
    .Input("handle: Ref(string)")
    .Input("flow_in: float")
    .Output("value: dtype")
    .Output("lengths: int64")
    .Attr("dtype: type")
    .Doc(R"doc(
Concat the elements from the TensorArray into value `value`.

Takes `T` elements of shapes

  ```
  (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
  ```

and concatenates them into a Tensor of shape:

  ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```

All elements must have the same shape (excepting the first dimension).

handle: The handle to a TensorArray.
dtype: The type of the elem that is returned.
flow_in: A float scalar that enforces proper chaining of operations.
value: All of the elements in the TensorArray, concatenated along the first
  axis.
lengths: A vector of the row sizes of the original T elements in the
  value output.  In the example above, this would be the values:
  `(n1, n2, ..., n(T-1))`.
)doc");

REGISTER_OP("TensorArraySplit")
    .Input("handle: Ref(string)")
    .Input("value: T")
    .Input("lengths: int64")
    .Input("flow_in: float")
    .Output("flow_out: float")
    .Attr("T: type")
    .Doc(R"doc(
Split the data from the input value into TensorArray elements.

Assuming that `lengths` takes on values

  ```(n0, n1, ..., n(T-1))```

and that `value` has shape

  ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```,

this splits values into a TensorArray with T tensors.

TensorArray index t will be the subtensor of values with starting position

  ```(n0 + n1 + ... + n(t-1), 0, 0, ...)```

and having size

  ```nt x d0 x d1 x ...```

handle: The handle to a TensorArray.
value: The concatenated tensor to write to the TensorArray.
lengths: The vector of lengths, how to split the rows of value into the
  TensorArray.
flow_in: A float scalar that enforces proper chaining of operations.
flow_out: A float scalar that enforces proper chaining of operations.
)doc");

REGISTER_OP("TensorArraySize")
    .Input("handle: Ref(string)")
    .Input("flow_in: float")
    .Output("size: int32")
    .Doc(R"doc(
Get the current size of the TensorArray.

handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
flow_in: A float scalar that enforces proper chaining of operations.
size: The current size of the TensorArray.
)doc");

REGISTER_OP("TensorArrayClose")
    .Input("handle: Ref(string)")
    .Doc(R"doc(
Delete the TensorArray from its resource container.  This enables
the user to close and release the resource in the middle of a step/run.

handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).
)doc");

// --------------------------------------------------------------------------

REGISTER_OP("LookupTableFind")
    .Input("table_handle: Ref(string)")
    .Input("keys: Tin")
    .Input("default_value: Tout")
    .Output("values: Tout")
    .Attr("Tin: type")
    .Attr("Tout: type")
    .Doc(R"doc(
Looks up keys in a table, outputs the corresponding values.

The tensor `keys` must of the same type as the keys of the table.
The output `values` is of the type of the table values.

The scalar `default_value` is the value output for keys not present in the
table. It must also be of the same type as the table values.

table_handle: Handle to the table.
keys:  Any shape.  Keys to look up.
values: Same shape as `keys`.  Values found in the table, or `default_values`
   for missing keys.
)doc");

REGISTER_OP("LookupTableSize")
    .Input("table_handle: Ref(string)")
    .Output("size: int64")
    .Doc(R"doc(
Computes the number of elements in the given table.

table_handle: Handle to the table.
size: Scalar that contains number of elements in the table.
)doc");

REGISTER_OP("HashTable")
    .Output("table_handle: Ref(string)")
    .Attr("container: string = ''")
    .Attr("shared_name: string = ''")
    .Attr("key_dtype: type")
    .Attr("value_dtype: type")
    .SetIsStateful()
    .Doc(R"doc(
Creates a non-initialized hash table.

This op creates a hash table, specifying the type of its keys and values.
Before using the table you will have to initialize it.  After initialization the
table will be immutable.

table_handle: Handle to a table.
container: If non-empty, this table is placed in the given container.
  Otherwise, a default container is used.
shared_name: If non-empty, this table is shared under the given name across
  multiple sessions.
key_dtype: Type of the table keys.
value_dtype: Type of the table values.
)doc");

REGISTER_OP("InitializeTable")
    .Input("table_handle: Ref(string)")
    .Input("keys: Tkey")
    .Input("values: Tval")
    .Attr("Tkey: type")
    .Attr("Tval: type")
    .Doc(R"doc(
Table initializer that takes two tensors for keys and values respectively.

table_handle: Handle to a table which will be initialized.
keys: Keys of type Tkey.
values: Values of type Tval. Same shape as `keys`.
)doc");

REGISTER_OP("GetSessionHandle")
    .Input("value: T")
    .Output("handle: string")
    .Attr("T: type")
    .Doc(R"doc(
Store the input tensor in the state of the current session.

value: The tensor to be stored.
handle: The handle for the tensor stored in the session state.
)doc");

REGISTER_OP("GetSessionTensor")
    .Input("handle: string")
    .Output("value: dtype")
    .Attr("dtype: type")
    .Doc(R"doc(
Get the value of the tensor specified by its handle.

handle: The handle for a tensor stored in the session state.
value: The tensor for the given handle.
dtype: The type of the output value.
)doc");

REGISTER_OP("DeleteSessionTensor")
    .Input("handle: string")
    .Doc(R"doc(
Delete the tensor specified by its handle in the session.

handle: The handle for a tensor stored in the session state.
)doc");

}  // namespace tensorflow
