//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//

#include "./MhloLegalizeUtils.h"
#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "torch-mlir/Conversion/TorchToMhlo/TorchToMhlo.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
#include <numeric>

using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;

namespace mlir {
namespace mhlo {

// Create a 32-bit float constant operator from a float
Value getMhloConstTensorSingleF32(PatternRewriter &rewriter, Operation *op,
                                  float val) {
  auto const_type = RankedTensorType::get({}, rewriter.getF32Type());
  auto const_attr = DenseElementsAttr::get(const_type, val);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

// Create a 64-bit float constant operator from a double
Value getMhloConstTensorSingleF64(PatternRewriter &rewriter, Operation *op,
                                  double val) {
  auto const_type = RankedTensorType::get({}, rewriter.getF64Type());
  auto const_attr = DenseElementsAttr::get(const_type, val);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

// Templated function to create a constant op for given type and shape.
// T: storage C type.
// Default template creates a constant tensor in T.
template <typename T>
llvm::Optional<Value> getConstTensor(PatternRewriter &rewriter, Operation *op,
                                     ArrayRef<T> vec, ArrayRef<int64_t> shape) {
  uint64_t num_total_elements = 1;
  for (int64_t a : shape) {
    num_total_elements *= a;
  }

  if (vec.size() != num_total_elements) {
    op->emitOpError("getConstTensor(): number of elements mismatch.");
    return llvm::None;
  }

  auto const_type =
      RankedTensorType::get(shape, rewriter.getIntegerType(sizeof(T) * 8));
  auto const_attr = DenseElementsAttr::get(const_type, vec);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

// Template specialization for APInt
template <>
llvm::Optional<Value> getConstTensor<APInt>(PatternRewriter &rewriter,
                                            Operation *op, ArrayRef<APInt> vec,
                                            ArrayRef<int64_t> shape) {
  uint64_t num_total_elements = 1;
  for (int64_t a : shape) {
    num_total_elements *= a;
  }

  if (vec.size() != num_total_elements) {
    op->emitOpError("getConstTensor(): number of elements mismatch.");
    return llvm::None;
  }
  auto const_type = RankedTensorType::get(
      shape, rewriter.getIntegerType(vec[0].getBitWidth()));
  auto const_attr = DenseElementsAttr::get(const_type, vec);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

// Template specialization for float
template <>
llvm::Optional<Value> getConstTensor<float>(PatternRewriter &rewriter,
                                            Operation *op, ArrayRef<float> vec,
                                            ArrayRef<int64_t> shape) {
  uint64_t num_total_elements = 1;
  for (int64_t a : shape) {
    num_total_elements *= a;
  }

  if (vec.size() != num_total_elements) {
    op->emitOpError("getConstTensor(): number of elements mismatch.");
    return llvm::None;
  }

  auto const_type = RankedTensorType::get(shape, rewriter.getF32Type());
  auto const_attr = DenseElementsAttr::get(const_type, vec);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

template <>
llvm::Optional<Value> getConstTensor<double>(PatternRewriter &rewriter,
                                            Operation *op, ArrayRef<double> vec,
                                            ArrayRef<int64_t> shape) {
  uint64_t num_total_elements = 1;
  for (int64_t a : shape) {
    num_total_elements *= a;
  }

  if (vec.size() != num_total_elements) {
    op->emitOpError("getConstTensor(): number of elements mismatch.");
    return llvm::None;
  }

  auto const_type = RankedTensorType::get(shape, rewriter.getF64Type());
  auto const_attr = DenseElementsAttr::get(const_type, vec);

  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}

// Template instantiation
template llvm::Optional<Value> getConstTensor<int32_t>(PatternRewriter &,
                                                       Operation *,
                                                       ArrayRef<int32_t> vec,
                                                       ArrayRef<int64_t> shape);

template llvm::Optional<Value> getConstTensor<int64_t>(PatternRewriter &,
                                                       Operation *,
                                                       ArrayRef<int64_t> vec,
                                                       ArrayRef<int64_t> shape);


template <typename T>
static bool isInValidRange(bool isFloat, const double &doubleValue, bool isInt,
                           const int64_t &intValue) {
  if (isFloat) {
    // Do a round-trip check here instead of numeric limits due to
    // compiler warnings around double <-> int conversion.
    return (doubleValue == static_cast<double>(static_cast<T>(doubleValue)));
  } else {
    assert(isInt);
    return (intValue >= std::numeric_limits<T>::min()) &&
           (intValue <= std::numeric_limits<T>::max());
  }
  return true;
}

template <typename T>
Value getSplatConstTensor(ConversionPatternRewriter &rewriter,
                          Operation *op,
                          T val,
                          Type dtype,
                          llvm::ArrayRef<int64_t> dshape) {
  auto const_type = RankedTensorType::get(
      dshape, dtype);
  auto const_attr = SplatElementsAttr::get(const_type, val);
  auto const_op =
      rewriter.create<mhlo::ConstantOp>(op->getLoc(), const_type, const_attr);
  return const_op.getResult();
}


LogicalResult torchScalarToMhloTensor(ConversionPatternRewriter &rewriter,
                                      Operation *op, Value torchScalarValue,
                                      Value &mhloTensor, Type dtype,
                                      llvm::ArrayRef<int64_t> dshape,
                                      bool doBroadcast) {
  // Retrieve a const float or int value but create the out Tensor with dtype.
  double doubleValue;
  auto isFloat =
      matchPattern(torchScalarValue, m_TorchConstantFloat(&doubleValue));

  int64_t intValue;
  auto isInt = matchPattern(torchScalarValue, m_TorchConstantInt(&intValue));

  if (!isFloat && !isInt)
    return op->emitError("Unable to extract the scalar constant");

  if (dtype.isa<mlir::FloatType>()) {
    if (doBroadcast) {
      mhloTensor = getSplatConstTensor<float>(rewriter, op,
                       (isFloat ? doubleValue : intValue),
                       dtype, dshape);
    } else {
      mhloTensor = mhlo::getConstTensor<float>(
                       rewriter, op, (isFloat ? doubleValue : intValue), dshape)
                       .getValue();
    }
  } else if (auto intType = dtype.dyn_cast<mlir::IntegerType>()) {
    auto w = intType.getWidth();
    if (w != 32 && w != 64)
      return op->emitError("Unsupported integer type") << intType;

    if (w == 32) {
      if (!isInValidRange<int32_t>(isFloat, doubleValue, isInt, intValue)) {
        return op->emitError("Supplied value of scalar constant exceeds limits "
                             "of destination type");
      }
      int32_t d = isFloat ? static_cast<int32_t>(doubleValue)
                          : static_cast<int32_t>(intValue);
      if (doBroadcast) {
        mhloTensor = getSplatConstTensor<int32_t>(rewriter, op, d, dtype, dshape);
      } else {
        mhloTensor =
            mhlo::getConstTensor<int32_t>(rewriter, op, {d}, dshape).getValue();
      }
    } else if (w == 64) {
      if (!isInValidRange<int64_t>(isFloat, doubleValue, isInt, intValue)) {
        return op->emitError("Supplied value of scalar constant exceeds limits "
                             "of destination type");
      }
      int64_t d = (isFloat ? static_cast<int64_t>(doubleValue) : intValue);
      if (doBroadcast) {
        mhloTensor = getSplatConstTensor<int64_t>(rewriter, op, d, dtype, dshape);
      } else {
        mhloTensor =
            mhlo::getConstTensor<int64_t>(rewriter, op, {d}, dshape).getValue();
      }
    }
  } else
    return op->emitError("Usupported element type");

  return success();
}


LogicalResult torchAlphaToMhloTensor(ConversionPatternRewriter &rewriter,
                                     Operation *op, Value alphaScalar,
                                     Value &alphaTensor, Type dtype,
                                     llvm::ArrayRef<int64_t> dshape,
                                     bool checkForUnity) {
  if (succeeded(torchScalarToMhloTensor(rewriter, op, alphaScalar, alphaTensor,
                                        dtype, dshape)))
    return success();

  // `alpha` has not been specified.
  int64_t alphaValue;
  if (!matchPattern(alphaScalar, m_TorchConstantInt(&alphaValue)))
    return op->emitError("Currently only scalar constants are supported for "
                         "alpha in MHLO operation");
  // When no alpha has been specified, this must be 1.
  if (checkForUnity && alphaValue != 1)
    return op->emitError("Unsupported integer value for alpha");

  alphaTensor =
      mlir::mhlo::getMhloConstTensorSingleF32(rewriter, op, alphaValue);

  return success();
}


Value promoteAndBroadcast(ConversionPatternRewriter &rewriter,
                          Value input, TensorType outType) {
  // Two tensors are “broadcastable” if the following rules hold:
  //   - Each tensor has at least one dimension.
  //   - When iterating over the dimension sizes, starting at the trailing dimension,
  //   the dimension sizes must either be equal, one of them is 1, or one of them
  //   does not exist.
  Operation* op = input.getDefiningOp();
  TensorType in_type = input.getType().dyn_cast<TensorType>();

  if (in_type.getElementType() != outType.getElementType()) {
    TensorType promoted_type = in_type.cloneWith(in_type.getShape(), outType.getElementType());
    input = rewriter.create<mhlo::ConvertOp>(op->getLoc(), promoted_type, input);
  }

  ArrayRef<int64_t> inShape = in_type.getShape();
  ArrayRef<int64_t> outShape = outType.getShape();

  bool do_bcast = (inShape.size() != outShape.size());
  SmallVector<int64_t> bcastDims;
  for (size_t i = 0; i < inShape.size(); ++i) {
    // iterating over the dimension sizes, starting at the trailing dimension
    size_t outPos = outShape.size() - 1 - i;
    size_t inPos = inShape.size() - 1 - i;
    int64_t outDim = outShape[outPos];
    int64_t inDim = inShape[inPos];
    if (inDim == outDim) {
      bcastDims.push_back(outPos);
    } else if (inDim != outDim && inDim == 1) {
      bcastDims.push_back(outPos);
      do_bcast = true;
    } else {
      op->emitError("The size of tensor a (") << inDim << ")"
          << "must match the size of tensor b (" << outDim << ")"
          << "at non-singleton dimension " << inPos;
    }
  }
  std::reverse(bcastDims.begin(), bcastDims.end());
  if (!do_bcast) {
    return input;
  }
  DenseIntElementsAttr bcast_attr = DenseIntElementsAttr::get(
      RankedTensorType::get({static_cast<long int>(bcastDims.size())}, rewriter.getI64Type()),
      bcastDims);
  auto bcast_op =
      rewriter.create<mhlo::BroadcastInDimOp>(op->getLoc(), outType, input, bcast_attr);
  return bcast_op.getResult();
}

SmallVector<size_t> toPositiveDims(ArrayRef<int64_t> dims, int64_t rank) {
  SmallVector<size_t> posDims;
  posDims.reserve(rank);
  std::transform(
      dims.begin(), dims.end(), std::back_inserter(posDims),
      [rank](int64_t d) -> size_t { return toPositiveDim(d, rank); });
  return posDims;
}

FailureOr<SmallVector<Value, 4>>
getDimSizesOfTensor(PatternRewriter &rewriter, Operation *op, Value value,
                    ArrayRef<int64_t> inpDims) {
  auto valueTy = value.getType().dyn_cast<RankedTensorType>();
  if (!valueTy) {
    return rewriter.notifyMatchFailure(
        op, "getDimSizesOfTensor(): the input is not a ranked tensor");
  }

  auto rank = valueTy.getRank();
  auto dims = toPositiveDims(inpDims, rank);
  SmallVector<Value, 4> dimSizes;
  dimSizes.reserve(dims.size());

  auto loc = op->getLoc();
  for (auto d : dims) {
    dimSizes.emplace_back(rewriter.create<arith::IndexCastOp>(
        loc, rewriter.getIntegerType(kMhloDimSizeBits),
        rewriter.create<tensor::DimOp>(loc, value, d)));
  }
  return dimSizes;
}

FailureOr<SmallVector<Value, 4>>
getDimSizesOfTensor(PatternRewriter &rewriter, Operation *op, Value value) {
  auto valueTy = value.getType().dyn_cast<RankedTensorType>();
  if (!valueTy) {
    return rewriter.notifyMatchFailure(
        op, "getDimSizesOfTensor(): the input is not a ranked tensor");
  }

  auto rank = valueTy.getRank();
  // Get int vector [0, 1, ..., rank-1]
  std::vector<int64_t> dims(rank);
  std::iota(dims.begin(), dims.end(), 0);
  return getDimSizesOfTensor(rewriter, op, value, dims);
}

FailureOr<Value> unsqueezeTensor(PatternRewriter &rewriter, Operation *op,
                                 Value tensor,
                                 ArrayRef<int64_t> inputUnsqzDims) {
  // Returns a new tensor with dims of size 1 inserted at the specified
  // position.
  //
  // The position indices (must be high to low dimension number of the returned
  // tensor) are specified with unsqzDims. Indices must be in-order, and in
  // range of tensor rank. Thus, unsqueeze a rank 1 tensor with {0, 2}, {0, 1,
  // 3}, {0, 1, 2} are all valid dimension sets, but {0, 3}, {2} are not.
  auto dimSizesInfo = getDimSizesOfTensor(rewriter, op, tensor);
  if (failed(dimSizesInfo))
    return rewriter.notifyMatchFailure(
        op, "failed to get dimension sizes of the input");

  auto dimSizes = *dimSizesInfo;
  auto rank = dimSizes.size();
  size_t newRank = rank + inputUnsqzDims.size();
  auto unsqzDims = toPositiveDims(inputUnsqzDims, newRank);
  for (size_t k = 0, sz = unsqzDims.size(); k < sz; ++k)
    if (k > 1 && unsqzDims[k] <= unsqzDims[k - 1])
      return rewriter.notifyMatchFailure(
          op, "unsqueeze dimensions must be specified in order");

  auto loc = op->getLoc();
  auto rankTy = tensor.getType().dyn_cast<RankedTensorType>();
  auto oldShape = rankTy.getShape();
  Type intType = rewriter.getIntegerType(kMhloDimSizeBits);
  auto one = rewriter.create<arith::ConstantOp>(
      loc, rewriter.getIntegerAttr(intType, 1));

  std::vector<Value> newDimSizes;
  std::vector<int64_t> newShape;
  newDimSizes.reserve(newRank);
  newShape.reserve(newRank);
  for (size_t k = 0, i = 0, j = 0; k < newRank; ++k) {
    if (j < unsqzDims.size() && unsqzDims[j] == k) {
      newDimSizes.push_back(one);
      newShape.push_back(1);
      j++;
    } else {
      newDimSizes.push_back(dimSizes[i]);
      newShape.push_back(oldShape[i]);
      i++;
    }
  }

  auto outTy = RankedTensorType::get(newShape, rankTy.getElementType());
  auto mhloShape = rewriter.create<tensor::FromElementsOp>(loc, newDimSizes);
  return rewriter.create<mhlo::DynamicReshapeOp>(loc, outTy, tensor, mhloShape)
      .getResult();
}

} // namespace mhlo
} // namespace mlir