/* Copyright 2019 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/mlir/tensorflow/utils/convert_type.h"

#include <limits>

#include "absl/strings/str_cat.h"
#include "llvm/Support/Casting.h"
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/Types.h"  // from @llvm-project
#include "mlir/Support/DebugStringHelper.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"

namespace tensorflow {

using mlir::Builder;
using mlir::ShapedType;
using mlir::Type;

absl::Status ConvertDataType(DataType dtype, Builder builder, Type* type) {
  switch (dtype) {
    case DT_HALF:
      *type = builder.getF16Type();
      return absl::OkStatus();
    case DT_FLOAT:
      *type = builder.getF32Type();
      return absl::OkStatus();
    case DT_DOUBLE:
      *type = builder.getF64Type();
      return absl::OkStatus();
    case DT_BOOL:
      *type = builder.getIntegerType(1);
      return absl::OkStatus();
    case DT_INT8:
      *type = builder.getIntegerType(8);
      return absl::OkStatus();
    case DT_INT16:
      *type = builder.getIntegerType(16);
      return absl::OkStatus();
    case DT_INT32:
      *type = builder.getIntegerType(32);
      return absl::OkStatus();
    case DT_INT64:
      *type = builder.getIntegerType(64);
      return absl::OkStatus();
    case DT_UINT8:
      *type = builder.getIntegerType(8, /*isSigned=*/false);
      return absl::OkStatus();
    case DT_UINT16:
      *type = builder.getIntegerType(16, /*isSigned=*/false);
      return absl::OkStatus();
    case DT_UINT32:
      *type = builder.getIntegerType(32, /*isSigned=*/false);
      return absl::OkStatus();
    case DT_UINT64:
      *type = builder.getIntegerType(64, /*isSigned=*/false);
      return absl::OkStatus();
    case DT_BFLOAT16:
      *type = builder.getBF16Type();
      return absl::OkStatus();
    case DT_COMPLEX64:
      *type = mlir::ComplexType::get(builder.getF32Type());
      return absl::OkStatus();
    case DT_COMPLEX128:
      *type = mlir::ComplexType::get(builder.getF64Type());
      return absl::OkStatus();
    case tensorflow::DT_FLOAT8_E4M3FN:
      *type = builder.getType<mlir::Float8E4M3FNType>();
      return absl::OkStatus();
    case tensorflow::DT_FLOAT8_E5M2:
      *type = builder.getType<mlir::Float8E5M2Type>();
      return absl::OkStatus();
    case tensorflow::DT_FLOAT8_E4M3FNUZ:
      *type = builder.getType<mlir::Float8E4M3FNUZType>();
      return absl::OkStatus();
    case tensorflow::DT_FLOAT8_E4M3B11FNUZ:
      *type = builder.getType<mlir::Float8E4M3B11FNUZType>();
      return absl::OkStatus();
    case tensorflow::DT_FLOAT8_E5M2FNUZ:
      *type = builder.getType<mlir::Float8E5M2FNUZType>();
      return absl::OkStatus();
    case DT_INT4:
      *type = builder.getIntegerType(4, /*isSigned=*/true);
      return absl::OkStatus();
    case DT_UINT4:
      *type = builder.getIntegerType(4, /*isSigned=*/false);
      return absl::OkStatus();
#define HANDLE_TF_TYPE(tftype, enumerant, name)             \
  case DT_##enumerant:                                      \
    *type = builder.getType<mlir::tf_type::tftype##Type>(); \
    return OkStatus();
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.def"

    default:
      return errors::Unimplemented(absl::StrCat(
          "Converting DataType '", DataTypeString(dtype), "' to MLIR Type"));
  }
}

absl::Status ConvertScalarTypeToDataType(Type type, DataType* dtype) {
  if (type.isF16()) {
    *dtype = DT_HALF;
    return absl::OkStatus();
  } else if (type.isF32()) {
    *dtype = DT_FLOAT;
    return absl::OkStatus();
  } else if (type.isF64()) {
    *dtype = DT_DOUBLE;
    return absl::OkStatus();
  } else if (type.isBF16()) {
    *dtype = DT_BFLOAT16;
    return absl::OkStatus();
  } else if (llvm::isa<mlir::Float8E4M3FNType>(type)) {
    *dtype = DT_FLOAT8_E4M3FN;
    return absl::OkStatus();
  } else if (llvm::isa<mlir::Float8E5M2Type>(type)) {
    *dtype = DT_FLOAT8_E5M2;
    return absl::OkStatus();
  } else if (llvm::isa<mlir::Float8E4M3FNUZType>(type)) {
    *dtype = DT_FLOAT8_E4M3FNUZ;
    return absl::OkStatus();
  } else if (llvm::isa<mlir::Float8E4M3B11FNUZType>(type)) {
    *dtype = DT_FLOAT8_E4M3B11FNUZ;
    return absl::OkStatus();
  } else if (llvm::isa<mlir::Float8E5M2FNUZType>(type)) {
    *dtype = DT_FLOAT8_E5M2FNUZ;
    return absl::OkStatus();
  } else if (auto itype = mlir::dyn_cast<mlir::IntegerType>(type)) {
    switch (itype.getWidth()) {
      case 1:
        *dtype = DT_BOOL;
        return absl::OkStatus();
      case 4:
        *dtype = itype.isUnsigned() ? DT_UINT4 : DT_INT4;
        return absl::OkStatus();
      case 8:
        *dtype = itype.isUnsigned() ? DT_UINT8 : DT_INT8;
        return absl::OkStatus();
      case 16:
        *dtype = itype.isUnsigned() ? DT_UINT16 : DT_INT16;
        return absl::OkStatus();
      case 32:
        *dtype = itype.isUnsigned() ? DT_UINT32 : DT_INT32;
        return absl::OkStatus();
      case 64:
        *dtype = itype.isUnsigned() ? DT_UINT64 : DT_INT64;
        return absl::OkStatus();
      default:
        return errors::Unimplemented(
            absl::StrCat("Converting ", debugString(type), " to DataType"));
    }
  } else if (auto complex_type = mlir::dyn_cast<mlir::ComplexType>(type)) {
    auto etype = complex_type.getElementType();
    if (etype.isF32()) {
      *dtype = DT_COMPLEX64;
      return absl::OkStatus();
    } else if (etype.isF64()) {
      *dtype = DT_COMPLEX128;
      return absl::OkStatus();
    }
    return errors::Unimplemented(
        absl::StrCat("Converting ", debugString(type), " to DataType"));
  }

#define HANDLE_TF_TYPE(tftype, enumerant, name)       \
  if (llvm::isa<mlir::tf_type::tftype##Type>(type)) { \
    *dtype = DT_##enumerant;                          \
    return OkStatus();                                \
  }
// NOLINTNEXTLINE
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.def"

  return errors::Unimplemented(
      absl::StrCat("Converting ", debugString(type), " to DataType"));
}

absl::Status ConvertToDataType(Type type, DataType* dtype) {
  if (auto stype = mlir::dyn_cast<ShapedType>(type)) {
    TF_RETURN_IF_ERROR(
        ConvertScalarTypeToDataType(stype.getElementType(), dtype));
  } else {
    TF_RETURN_IF_ERROR(ConvertScalarTypeToDataType(type, dtype));
  }
  return absl::OkStatus();
}

void ConvertToMlirShape(const TensorShape& input_shape,
                        llvm::SmallVectorImpl<int64_t>* shape) {
  shape->reserve(input_shape.dims());
  for (const auto& d : input_shape) {
    shape->push_back(d.size == kTFDynamicSize ? ShapedType::kDynamic : d.size);
  }
}

absl::Status ConvertToMlirShape(const TensorShapeProto& input_shape,
                                llvm::SmallVectorImpl<int64_t>* shape) {
  shape->reserve(input_shape.dim_size());
  auto& dims = input_shape.dim();
  for (auto& d : dims) {
    if (d.size() > std::numeric_limits<int64_t>::max()) {
      return errors::InvalidArgument("Shape element overflows");
    }
    shape->push_back(d.size() == kTFDynamicSize ? ShapedType::kDynamic
                                                : d.size());
  }
  return absl::OkStatus();
}

absl::StatusOr<mlir::Type> ConvertToMlirTensorType(
    const TensorShapeProto& shape, DataType dtype, mlir::Builder* builder) {
  mlir::Type element_type;
  TF_RETURN_IF_ERROR(ConvertDataType(dtype, *builder, &element_type));
  if (shape.unknown_rank()) {
    return mlir::UnrankedTensorType::get(element_type);
  }
  llvm::SmallVector<int64_t, 4> shape_dims;
  TF_RETURN_IF_ERROR(ConvertToMlirShape(shape, &shape_dims));
  return GetTypeFromTFTensorShape(shape_dims, element_type);
}

}  // namespace tensorflow
