#include "mlir/Transforms/DialectConversion.h"
#include "src/Accelerators/PIM/Common/PimCommon.hpp"
#include "src/Accelerators/PIM/Common/Support/CheckedArithmetic.hpp"
#include "src/Accelerators/PIM/Conversion/SpatialToPim/Common.hpp"
#include "src/Accelerators/PIM/Conversion/SpatialToPim/Patterns.hpp"
#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"

using namespace mlir;

namespace onnx_mlir {
namespace raptor {

#include "src/Accelerators/PIM/Conversion/SpatialToPim/SpatialToPim.hpp.inc"

} // namespace raptor

struct LowerFragmentAssemblyBlueprintPattern
  : OpConversionPattern<spatial::SpatBlueprintOp> {
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(spatial::SpatBlueprintOp op,
                                OpAdaptor adaptor,
                                ConversionPatternRewriter& rewriter) const override {
    if (!spatial::isFragmentAssembly(op.getMode()))
      return failure();

    auto resultType = dyn_cast<ShapedType>(op.getOutput().getType());
    if (!resultType || !resultType.hasStaticShape())
      return op.emitOpError("fragment assembly lowering requires a static ranked tensor result");

    std::optional<ArrayRef<int64_t>> operandIndicesAttr = op.getFragmentOperandIndices();
    std::optional<ArrayRef<int64_t>> sourceOffsetsAttr = op.getFragmentSourceOffsets();
    std::optional<ArrayRef<int64_t>> fragmentStridesAttr = op.getFragmentStrides();
    if (!operandIndicesAttr || !sourceOffsetsAttr || !fragmentStridesAttr)
      return op.emitOpError("fragment assembly lowering requires explicit fragment metadata");

    ArrayRef<int64_t> operandIndices = *operandIndicesAttr;
    ArrayRef<int64_t> sourceOffsets = *sourceOffsetsAttr;
    ArrayRef<int64_t> flatOffsets = op.getFragmentOffsets();
    ArrayRef<int64_t> flatSizes = op.getFragmentSizes();
    ArrayRef<int64_t> flatStrides = *fragmentStridesAttr;
    int64_t rank = resultType.getRank();

    SmallVector<Value> fragmentOperands {adaptor.getInput()};
    llvm::append_range(fragmentOperands, adaptor.getFragments());
    if (failed(validateFragmentAssemblyMetadata(
          op, rank, fragmentOperands.size(), operandIndices, sourceOffsets, flatOffsets, flatSizes, flatStrides)))
      return failure();

    if (op.getIndexMap() == spatial::kContiguousRowMajorFragments) {
      if (!spatial::isCanonicalContiguousRowMajorFragmentAssembly(op))
        return op.emitOpError("contiguous row-major fragment physical source order or storage is not canonical");
      auto reshaped = reshapeContiguousRowMajorFragments(
        rewriter, op.getLoc(), adaptor.getInput(), cast<RankedTensorType>(resultType));
      if (failed(reshaped))
        return op.emitOpError("contiguous row-major fragment storage does not match its logical result");
      rewriter.replaceOp(op, *reshaped);
      return success();
    }
    Value currentOutput =
      tensor::EmptyOp::create(rewriter, op.getLoc(), resultType.getShape(), resultType.getElementType()).getResult();
    for (int64_t fragmentIndex = 0; fragmentIndex < static_cast<int64_t>(operandIndices.size()); ++fragmentIndex) {
      int64_t operandIndex = operandIndices[fragmentIndex];

      SmallVector<int64_t, 4> fragmentOffsets;
      for (int64_t dim = 0; dim < rank; ++dim) {
        int64_t flatIndex = fragmentIndex * rank + dim;
        if (flatStrides[flatIndex] != 1)
          return op.emitOpError("fragment assembly lowering only supports unit strides");
        fragmentOffsets.push_back(flatOffsets[flatIndex]);
      }

      Value source = fragmentOperands[operandIndex];
      auto sourceType = dyn_cast<RankedTensorType>(source.getType());
      if (!sourceType || !sourceType.hasStaticShape())
        return op.emitOpError("fragment assembly lowering requires static ranked tensor operands");

      SmallVector<int64_t, 4> fragmentShape;
      fragmentShape.reserve(rank);
      for (int64_t dim = 0; dim < rank; ++dim)
        fragmentShape.push_back(flatSizes[fragmentIndex * rank + dim]);

      Value fragment = source;
      if (llvm::to_vector(sourceType.getShape()) != fragmentShape || sourceOffsets[fragmentIndex] != 0) {
        FailureOr<SmallVector<int64_t, 4>> extractOffsets = getStaticSliceOffsetsForElementOffset(
          op, sourceType, fragmentShape, sourceOffsets[fragmentIndex], "fragment assembly source slice");
        if (failed(extractOffsets))
          return failure();
        fragment = tensor::ExtractSliceOp::create(rewriter,
                                                  op.getLoc(),
                                                  source,
                                                  getStaticIndexAttrs(rewriter, *extractOffsets),
                                                  getStaticIndexAttrs(rewriter, fragmentShape),
                                                  getUnitStrides(rewriter, rank));
      }

      currentOutput = tensor::InsertSliceOp::create(rewriter,
                                                    op.getLoc(),
                                                    fragment,
                                                    currentOutput,
                                                    getStaticIndexAttrs(rewriter, fragmentOffsets),
                                                    getStaticIndexAttrs(rewriter, fragmentShape),
                                                    getUnitStrides(rewriter, rank))
                        .getResult();
    }

    rewriter.replaceOp(op, currentOutput);
    return success();
  }
};

void populateInitialPatterns(RewritePatternSet& patterns) {
  raptor::populateWithGenerated(patterns);
  populateTransposeLoweringPatterns(patterns);
}

void populateCoreBodyPatterns(RewritePatternSet& patterns) {
  raptor::populateWithGenerated(patterns);
  populateTransposeLoweringPatterns(patterns);
  patterns.add<LowerFragmentAssemblyBlueprintPattern>(patterns.getContext());
}

} // namespace onnx_mlir