/* Copyright 2017 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/compiler/tf2xla/type_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/primitive_util.h"
#include "tensorflow/core/framework/kernel_def_builder.h"

namespace tensorflow {
namespace {

class CastOp : public XlaOpKernel {
 public:
  explicit CastOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("SrcT", &src_dtype_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("DstT", &dst_dtype_));
    OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(src_dtype_, &src_type_));
    OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(dst_dtype_, &dst_type_));
  }

  void Compile(XlaOpKernelContext* ctx) override {
    xla::XlaBuilder* builder = ctx->builder();
    xla::XlaOp input = ctx->Input(0);
    xla::XlaOp output;

    if (src_dtype_ == dst_dtype_) {
      output = input;
    } else if (dst_dtype_ == DT_BOOL) {
      output = builder->Ne(input, XlaHelpers::Zero(builder, src_dtype_));
    } else if (xla::primitive_util::IsComplexType(src_type_) &&
               !xla::primitive_util::IsComplexType(dst_type_)) {
      // As in cast_op.h, we replicate the numpy behavior of truncating the
      // imaginary part.
      output = builder->ConvertElementType(builder->Real(input), dst_type_);
    } else {
      output = builder->ConvertElementType(input, dst_type_);
    }

    ctx->SetOutput(0, output);
  }

 protected:
  DataType src_dtype_, dst_dtype_;
  xla::PrimitiveType src_type_, dst_type_;

  TF_DISALLOW_COPY_AND_ASSIGN(CastOp);
};

REGISTER_XLA_OP(Name("Cast"), CastOp);

class BitcastOp : public XlaOpKernel {
 public:
  explicit BitcastOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &src_dtype_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("type", &dst_dtype_));
    OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(src_dtype_, &src_type_));
    OP_REQUIRES_OK(ctx, DataTypeToPrimitiveType(dst_dtype_, &dst_type_));
  }

  void Compile(XlaOpKernelContext* ctx) override {
    xla::XlaBuilder* builder = ctx->builder();
    xla::XlaOp input = ctx->Input(0);
    xla::XlaOp output;

    if (src_dtype_ == dst_dtype_) {
      output = input;
    } else {
      // The only complex type in XLA is C64, so error out if the bitcast has a
      // complex source or destination type and the bitcast is not trivial.
      OP_REQUIRES(ctx,
                  !xla::primitive_util::IsComplexType(src_type_) &&
                      !xla::primitive_util::IsComplexType(dst_type_),
                  errors::Unimplemented("Complex types not supported."));
      // XLA bitcast requires that the bit-width of the source and destination
      // matches, and currently only the simple lowering is performed.
      OP_REQUIRES(ctx,
                  xla::primitive_util::BitWidth(src_type_) ==
                      xla::primitive_util::BitWidth(dst_type_),
                  errors::Unimplemented(
                      "Only bitcasts between equally sized types supported."));
      output = builder->BitcastConvertType(input, dst_type_);
    }

    ctx->SetOutput(0, output);
  }

 protected:
  DataType src_dtype_, dst_dtype_;
  xla::PrimitiveType src_type_, dst_type_;

  TF_DISALLOW_COPY_AND_ASSIGN(BitcastOp);
};

REGISTER_XLA_OP(Name("Bitcast"), BitcastOp);

}  // anonymous namespace
}  // namespace tensorflow
