// expected-warning @+1 {{'strides' attribute for MaxUnpooling2D does not contain integer values}}
func.func private @max_unpooling_2d_strides_wrong_type(%arg0: tensor<1x1x2x1xf32>, %arg1: tensor<1x1x2x1xi32>) -> tensor<1x2x4x1xf32> attributes {tf._implements = #tf_type.func<@"addons:MaxUnpooling2D", {padding = "SAME", pool_size = [2, 2], strides = ["2", "2"]}>}
}

// -----

                                     1, 1, 1, 1, 'SAME', 'RELU')

    # Max pooling. The kernel size spec {ksize} also follows the layout of
    # the data. Here we have a pooling window of 2, and a stride of 2.
    # output shape: [-1, 14, 14, 32]
    max_pool1 = gen_mnist_ops.new_max_pool(conv1, 2, 2, 2, 2, 'SAME')

    # output shape: [-1, 14, 14, 64]
    conv2 = gen_mnist_ops.new_conv2d(max_pool1, self.weights['f2'],

    %2 = stablehlo.convolution(%1, %0)
      dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
      window = {
        stride = [1, 1], pad = [[0, 0], [1, 1]],
        lhs_dilate = [1, 1],
        rhs_dilate = [1, 1]
      }
      {
        batch_group_count = 1 : i64,
        feature_group_count = 1 : i64

      first.getType().clone(new_slice_shape), first.getOperand(),
      /*start_indices=*/rewriter.getI64TensorAttr(new_start),
      /*limit_indices=*/rewriter.getI64TensorAttr(new_limit),
      /*strides=*/first.getStrides());

  SmallVector<Value> new_concat_values;
  new_concat_values.reserve(concat.getVal().size() - 1);
  new_concat_values.push_back(new_slice);

    // CHECK: %[[SLICE0:.*]] = "mhlo.slice"(%[[ARG2]])
    // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>
    // CHECK-DAG-SAME: limit_indices = dense<1> : tensor<1xi64>
    // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>
    // CHECK-SAME: (tensor<2xi32>) -> tensor<1xi32>
    // CHECK: %[[DIM0:.*]] = mhlo.reshape %[[SLICE0]] : (tensor<1xi32>) -> tensor<i32>

    // CHECK: %[[SLICE1:.*]] = "mhlo.slice"(%[[ARG2]])

  // CHECK: mhlo.convolution(%arg0, %[[CONST]])
  // CHECK-SAME{LITERAL}: dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
  // CHECK-SAME{LITERAL}: window = {stride = [1, 2], pad = [[0, 0], [0, 0]], lhs_dilate = [1, 1], rhs_dilate = [2, 2]}
  // CHECK-SAME{LITERAL}: batch_group_count = 1 : i64, feature_group_count = 1 : i64

      StridedSliceGrad(scope, x, begin, end, strides, grad_inputs[0],
                       StridedSliceGrad::BeginMask(begin_mask)
                           .EndMask(end_mask)
                           .EllipsisMask(ellipsis_mask)
                           .NewAxisMask(new_axis_mask)
                           .ShrinkAxisMask(shrink_axis_mask)));
  // No gradients returned for begin, end and strides
  grad_outputs->push_back(NoGradient());

		}
		if r <= range_.Hi {
			return (r-range_.Lo)%range_.Stride == 0
		}
	}
	return false
}

func binary(ranges []Range16, r uint16) bool {
	// binary search over ranges
	lo := 0
	hi := len(ranges)
	for lo < hi {
		m := int(uint(lo+hi) >> 1)
		range_ := &ranges[m]
		if range_.Lo <= r && r <= range_.Hi {
			return (r-range_.Lo)%range_.Stride == 0
		}
		if r < range_.Lo {
			hi = m
		} else {

    } else {
      return next >>> 1;
    }
  }

  public void testCrc32cStrideTable() {
    int next = CRC32C_GENERATOR_FLIPPED;
    for (int i = 0; i < 12; i++) { // for 3 ints = 12 bytes in between each stride window
      next = (next >>> 8) ^ Crc32cHashFunction.Crc32cHasher.BYTE_TABLE[next & 0xFF];
    }
    int[][] expected = new int[4][256];
    for (int b = 0; b < 4; ++b) {
      for (int bit = 128; bit != 0; bit >>= 1) {

	bw := blockWriter{e: e}
	bw.setup()
	lzww := lzw.NewWriter(bw, lzw.LSB, litWidth)
	if dx := b.Dx(); dx == pm.Stride {
		_, e.err = lzww.Write(pm.Pix[:dx*b.Dy()])
		if e.err != nil {
			lzww.Close()
			return
		}
	} else {
		for i, y := 0, b.Min.Y; y < b.Max.Y; i, y = i+pm.Stride, y+1 {
			_, e.err = lzww.Write(pm.Pix[i : i+dx])
			if e.err != nil {
				lzww.Close()
				return
			}
		}
	}