// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package math

// Dim returns the maximum of x-y or 0.
//
// Special cases are:
//
//	Dim(+Inf, +Inf) = NaN
//	Dim(-Inf, -Inf) = NaN
//	Dim(x, NaN) = Dim(NaN, x) = NaN
func Dim(x, y float64) float64 {
	// The special cases result in NaN after the subtraction:
	//      +Inf - +Inf = NaN
	//      -Inf - -Inf = NaN

    }
  }

  if (TF_GetCode(status) == TF_OK) {
    auto* dims = new int64_t[dim_count + 1];
    int new_dim_count = dim_count;
    for (int dim = 0; dim < dim_count; ++dim) {
      dims[dim] = TF_Dim(tensor, dim);
    }
    if (k->out_size < k->in_size) {
      dims[new_dim_count++] = static_cast<int64_t>(k->in_size / k->out_size);
    } else if (k->out_size > k->in_size) {

namespace tensorflow {
namespace {

std::string ConvertToMlirString(const std::vector<int64_t>& dims,
                                bool unknown_rank, DataType dtype) {
  TensorShapeProto shape;
  shape.set_unknown_rank(unknown_rank);
  for (int64_t dim : dims) {
    shape.add_dim()->set_size(dim);
  }
  mlir::MLIRContext context;
  mlir::Builder b(&context);

//   ```
//
//   This operation requires that:
//
//   `-1-input.dims() <= dim <= input.dims()`
//
//   This operation is related to `squeeze()`, which removes dimensions of
//   size 1.
Status ExpandDims(AbstractContext* ctx, AbstractTensorHandle* const input,
                  AbstractTensorHandle* const dim,
                  AbstractTensorHandle** output, const char* name,

//
// May return NULL (and invoke the deallocator) if the provided data buffer
// (data, len) is inconsistent with a tensor of the given TF_DataType
// and the shape specified by (dima, num_dims).
TF_CAPI_EXPORT extern TF_Tensor* TF_NewTensor(
    TF_DataType, const int64_t* dims, int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg);

  return shaped_type.hasStaticShape();
}

bool HasStaticShapeAtDims(Value value, const ArrayRef<int> dims) {
  auto shaped_type = mlir::dyn_cast<ShapedType>(value.getType());
  if (!shaped_type || !shaped_type.hasRank()) return false;

  for (auto dim : dims) {
    if (shaped_type.isDynamicDim(dim)) return false;
  }
  return true;
}

Type CloneTypeWithNewElementType(Type old_type, Type element_type) {

    // Collect non-unit dims and corresponding dim in the input shape.
    SmallVector<int64_t, 4> input_carryover_dims;
    SmallVector<int64_t, 4> non_unit_dims;

    for (int i = 0; i < input_shape_extended.size(); i++) {
      int64_t dim = broadcast_shape[i];
      if (dim != 1) {
        non_unit_dims.push_back(dim);
        input_carryover_dims.push_back(input_shape_extended[i]);
      }

  EXPECT_EQ(revived_tensor->Type(), expected.dtype());
  EXPECT_EQ(revived_tensor->NumElements(), expected.NumElements());
  EXPECT_EQ(revived_tensor->NumDims(), expected.dims());
  for (int i = 0; i < expected.dims(); ++i) {
    EXPECT_EQ(revived_tensor->Dim(i), expected.dim_size(i));
  }

  testing::CheckBufferDataIsEqual(expected.dtype(), expected.NumElements(),
                                  revived_tensor->Data(), expected.data());

  shape->reserve(input_shape.dims());
  for (const auto& d : input_shape) {
    shape->push_back(d.size == kTFDynamicSize ? ShapedType::kDynamic : d.size);
  }
}

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) {

				"dir/",
				"dir/file",
				"dir2/",
				"file2",
			},
			false,
		},
		{
			"mount",
			[]string{
				"dir/",
				"dir/file",
				"dir2/",
				"file2",
				"mount/",
				"mount/file3",
			},
			true,
		},
		{
			"innermount",
			[]string{
				"dir/",
				"dir/file",
				"dir/dir2/",
				"dir/dir2/file2",
				"dir/dir2/mount/",
				"dir/dir2/mount/file3",
			},