/* 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/graph/testlib.h"

#include <vector>
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/kernels/constant_op.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

// HostConst: forced to generate output on the host.
// Only used by testlib; no op is registered for this kernel
// externally (i.e., in array_ops.cc)
REGISTER_KERNEL_BUILDER(Name("HostConst").Device(DEVICE_CPU), HostConstantOp);
REGISTER_KERNEL_BUILDER(
    Name("HostConst").Device(DEVICE_GPU).HostMemory("output"), HostConstantOp);
#ifdef TENSORFLOW_USE_SYCL
REGISTER_KERNEL_BUILDER(
    Name("HostConst").Device(DEVICE_SYCL).HostMemory("output"), HostConstantOp);
#endif  // TENSORFLOW_USE_SYCL

// Register the HostConst Op
// Returns a constant tensor on the host.  Useful for writing C++ tests
// and benchmarks which run on GPU but require arguments pinned to the host.
// Used by test::graph::HostConstant.
// value: Attr `value` is the tensor to return.
REGISTER_OP("HostConst")
    .Output("output: dtype")
    .Attr("value: tensor")
    .Attr("dtype: type");

namespace test {
namespace graph {

Node* Send(Graph* g, Node* input, const string& tensor, const string& sender,
           const uint64 sender_incarnation, const string& receiver) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "_Send")
                  .Input(input, 0)
                  .Attr("tensor_name", tensor)
                  .Attr("send_device", sender)
                  .Attr("send_device_incarnation",
                        static_cast<int64>(sender_incarnation))
                  .Attr("recv_device", receiver)
                  .Finalize(g, &ret));
  return ret;
}

Node* Recv(Graph* g, const string& tensor, const string& type,
           const string& sender, const uint64 sender_incarnation,
           const string& receiver) {
  Node* ret;
  DataType dtype;
  CHECK(DataTypeFromString(type, &dtype));
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "_Recv")
                  .Attr("tensor_type", dtype)
                  .Attr("tensor_name", tensor)
                  .Attr("send_device", sender)
                  .Attr("send_device_incarnation",
                        static_cast<int64>(sender_incarnation))
                  .Attr("recv_device", receiver)
                  .Finalize(g, &ret));
  return ret;
}

Node* Constant(Graph* g, const Tensor& tensor) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Const")
                  .Attr("dtype", tensor.dtype())
                  .Attr("value", tensor)
                  .Finalize(g, &ret));
  return ret;
}

Node* Constant(Graph* g, const Tensor& tensor, const string& name) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(name, "Const")
                  .Attr("dtype", tensor.dtype())
                  .Attr("value", tensor)
                  .Finalize(g, &ret));
  return ret;
}

Node* HostConstant(Graph* g, const Tensor& tensor) {
  return HostConstant(g, tensor, g->NewName("n"));
}

Node* HostConstant(Graph* g, const Tensor& tensor, const string& name) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(name, "HostConst")
                  .Attr("dtype", tensor.dtype())
                  .Attr("value", tensor)
                  .Finalize(g, &ret));
  return ret;
}

Node* Var(Graph* g, const DataType dtype, const TensorShape& shape) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Variable")
                  .Attr("dtype", dtype)
                  .Attr("shape", shape)
                  .Finalize(g, &ret));
  return ret;
}

Node* Var(Graph* g, const DataType dtype, const TensorShape& shape,
          const string& name) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(name, "Variable")
                  .Attr("dtype", dtype)
                  .Attr("shape", shape)
                  .Finalize(g, &ret));
  return ret;
}

Node* Assign(Graph* g, Node* var, Node* val) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Assign")
                  .Input(var)
                  .Input(val)
                  .Attr("use_locking", true)
                  .Finalize(g, &ret));
  return ret;
}

Node* Reduce(Graph* g, const string& reduce, Node* data, Node* axes,
             bool keep_dims) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), reduce, g->op_registry())
                  .Input(data)
                  .Input(axes)
                  .Attr("keep_dims", keep_dims)
                  .Finalize(g, &ret));
  return ret;
}

Node* QuantizeToUINT8(Graph* g, Node* data) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Quantize")
                  .Input(data)
                  .Attr("T", DT_QUINT8)
                  .Attr("max_range", 1.0f)
                  .Attr("min_range", -1.0f)
                  .Finalize(g, &ret));
  return ret;
}

Node* Matmul(Graph* g, Node* in0, Node* in1, bool transpose_a,
             bool transpose_b) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "MatMul")
                  .Input(in0)
                  .Input(in1)
                  .Attr("transpose_a", transpose_a)
                  .Attr("transpose_b", transpose_b)
                  .Finalize(g, &ret));
  return ret;
}

Node* BatchMatmul(Graph* g, Node* in0, Node* in1, bool adj_x, bool adj_y) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "BatchMatMul")
                  .Input(in0)
                  .Input(in1)
                  .Attr("adj_x", adj_x)
                  .Attr("adj_y", adj_y)
                  .Finalize(g, &ret));
  return ret;
}

Node* RandomNumberGenerator(const string& op, Graph* g, Node* input,
                            DataType dtype) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), op, g->op_registry())
                  .Input(input)
                  .Attr("dtype", dtype)
                  .Attr("seed", 0)
                  .Finalize(g, &ret));
  return ret;
}

Node* RandomUniform(Graph* g, Node* input, DataType dtype) {
  return RandomNumberGenerator("RandomUniform", g, input, dtype);
}

Node* RandomGaussian(Graph* g, Node* input, DataType dtype) {
  return RandomNumberGenerator("RandomStandardNormal", g, input, dtype);
}

Node* TruncatedNormal(Graph* g, Node* input, DataType dtype) {
  return RandomNumberGenerator("TruncatedNormal", g, input, dtype);
}

Node* RandomGamma(Graph* g, Node* shape, Node* alpha) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "RandomGamma")
                  .Input(shape)
                  .Input(alpha)
                  .Attr("seed", 0)
                  .Finalize(g, &ret));
  return ret;
}

Node* RandomPoisson(Graph* g, Node* shape, Node* lam) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "RandomPoisson")
                  .Input(shape)
                  .Input(lam)
                  .Attr("seed", 0)
                  .Finalize(g, &ret));
  return ret;
}

Node* Unary(Graph* g, const string& func, Node* input, int index) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), func, g->op_registry())
                  .Input(input, index)
                  .Finalize(g, &ret));
  return ret;
}

Node* Binary(Graph* g, const string& func, Node* in0, Node* in1) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), func, g->op_registry())
                  .Input(in0)
                  .Input(in1)
                  .Finalize(g, &ret));
  return ret;
}

Node* Multi(Graph* g, const string& func, gtl::ArraySlice<Node*> ins) {
  Node* ret;
  auto b = NodeBuilder(g->NewName("n"), func, g->op_registry());
  for (Node* n : ins) b = b.Input(n);
  TF_CHECK_OK(b.Finalize(g, &ret));
  return ret;
}

Node* Identity(Graph* g, Node* input, int index) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Identity")
                  .Input(input, index)
                  .Finalize(g, &ret));
  return ret;
}

Node* Add(Graph* g, Node* in0, Node* in1) { return Binary(g, "Add", in0, in1); }

Node* Reverse(Graph* g, Node* tensor, Node* axis) {
  return Binary(g, "ReverseV2", tensor, axis);
}

Node* Roll(Graph* g, Node* input, Node* shift, Node* axis) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Roll", g->op_registry())
                  .Input(input)
                  .Input(shift)
                  .Input(axis)
                  .Finalize(g, &ret));
  return ret;
}

Node* Error(Graph* g, Node* input, const string& errmsg) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Error")
                  .Input(input)
                  .Attr("message", errmsg)
                  .Finalize(g, &ret));
  return ret;
}

Node* InvalidRefType(Graph* g, DataType out_type, DataType invalid_type) {
  DCHECK(out_type != invalid_type);
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "InvalidRefType")
                  .Attr("TIn", out_type)
                  .Attr("TOut", invalid_type)
                  .Finalize(g, &ret));
  return ret;
}

Node* Delay(Graph* g, Node* input, Microseconds delay_micros) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Delay")
                  .Input(input)
                  .Attr("micros", delay_micros.value())
                  .Finalize(g, &ret));
  return ret;
}

Node* NoOp(Graph* g, const std::vector<Node*>& control_inputs) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "NoOp")
                  .ControlInputs(control_inputs)
                  .Finalize(g, &ret));
  return ret;
}

Node* Switch(Graph* g, Node* in0, Node* in1) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Switch")
                  .Input(in0)
                  .Input(in1)
                  .Finalize(g, &ret));
  return ret;
}

Node* Enter(Graph* g, Node* input, const string& frame_name) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Enter")
                  .Input(input)
                  .Attr("frame_name", frame_name)
                  .Finalize(g, &ret));
  return ret;
}

Node* Exit(Graph* g, Node* input) {
  Node* ret;
  TF_CHECK_OK(
      NodeBuilder(g->NewName("n"), "Exit").Input(input).Finalize(g, &ret));
  return ret;
}

Node* Merge(Graph* g, Node* in0, Node* in1) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Merge")
                  .Input({in0, in1})
                  .Finalize(g, &ret));
  return ret;
}

Node* Merge(Graph* g, Node* in0, gtl::ArraySlice<string> remaining_in) {
  std::vector<NodeBuilder::NodeOut> inputs;
  inputs.reserve(remaining_in.size() + 1);
  inputs.emplace_back(in0);
  for (const string& in_name : remaining_in) {
    inputs.emplace_back(in_name, 0, inputs[0].dt);
  }

  Node* ret;
  TF_CHECK_OK(
      NodeBuilder(g->NewName("n"), "Merge").Input(inputs).Finalize(g, &ret));
  return ret;
}

Node* Concat(Graph* g, Node* concat_dim, gtl::ArraySlice<Node*> tensors) {
  std::vector<NodeBuilder::NodeOut> nodeouts;
  nodeouts.reserve(tensors.size());
  for (auto const t : tensors) {
    nodeouts.emplace_back(t);
  }
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Concat")
                  .Input(concat_dim)
                  .Input(nodeouts)
                  .Finalize(g, &ret));
  return ret;
}

Node* ConcatV2(Graph* g, gtl::ArraySlice<Node*> tensors, Node* concat_dim) {
  std::vector<NodeBuilder::NodeOut> nodeouts;
  nodeouts.reserve(tensors.size());
  for (auto const t : tensors) {
    nodeouts.emplace_back(t);
  }
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "ConcatV2")
                  .Input(nodeouts)
                  .Input(concat_dim)
                  .Finalize(g, &ret));
  return ret;
}

Node* Next(Graph* g, const string& name, Node* input) {
  Node* ret;
  TF_CHECK_OK(
      NodeBuilder(name, "NextIteration").Input(input).Finalize(g, &ret));
  return ret;
}

Node* LoopCond(Graph* g, Node* input) {
  Node* ret;
  TF_CHECK_OK(
      NodeBuilder(g->NewName("n"), "LoopCond").Input(input).Finalize(g, &ret));
  return ret;
}

Node* Less(Graph* g, Node* in0, Node* in1) {
  return Binary(g, "Less", in0, in1);
}

Node* Select(Graph* g, Node* c, Node* inx, Node* iny) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Select")
                  .Input(c)
                  .Input(inx)
                  .Input(iny)
                  .Finalize(g, &ret));
  return ret;
}

Node* Cast(Graph* g, Node* in, DataType dst) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Cast")
                  .Input(in)
                  .Attr("DstT", dst)
                  .Finalize(g, &ret));
  return ret;
}

Node* Gather(Graph* g, Node* in0, Node* in1, Node* axis) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "GatherV2")
                  .Input(in0)
                  .Input(in1)
                  .Input(axis)
                  .Finalize(g, &ret));
  return ret;
}

Node* GetSessionTensor(Graph* g, Node* in) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "GetSessionTensor")
                  .Input(in, 0)
                  .Attr("dtype", DT_FLOAT)
                  .Finalize(g, &ret));
  return ret;
}

Node* Relu(Graph* g, Node* in) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Relu")
                  .Input(in, 0)
                  .Attr("T", DT_FLOAT)
                  .Finalize(g, &ret));
  return ret;
}

Node* Relu6(Graph* g, Node* in) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Relu6")
                  .Input(in, 0)
                  .Attr("T", DT_FLOAT)
                  .Finalize(g, &ret));
  return ret;
}

Node* BiasAdd(Graph* g, Node* value, Node* bias) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "BiasAdd")
                  .Input(value)
                  .Input(bias)
                  .Attr("T", DT_FLOAT)
                  .Finalize(g, &ret));
  return ret;
}

Node* Conv2D(Graph* g, Node* in0, Node* in1) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Conv2D")
                  .Input(in0)
                  .Input(in1)
                  .Attr("T", DT_FLOAT)
                  .Attr("strides", {1, 1, 1, 1})
                  .Attr("padding", "SAME")
                  .Finalize(g, &ret));
  return ret;
}

Node* Diag(Graph* g, Node* in, DataType type) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "Diag")
                  .Input(in)
                  .Attr("T", type)
                  .Finalize(g, &ret));
  return ret;
}

Node* DiagPart(Graph* g, Node* in, DataType type) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "DiagPart")
                  .Input(in)
                  .Attr("T", type)
                  .Finalize(g, &ret));
  return ret;
}

void ToGraphDef(Graph* g, GraphDef* gdef) { g->ToGraphDef(gdef); }

}  // end namespace graph
}  // end namespace test
}  // end namespace tensorflow
