//
// See Ulf Adams, "Ryū: Fast Float-to-String Conversion" (doi:10.1145/3192366.3192369)
//
// Fixed precision formatting is a variant of the original paper's
// algorithm, where a single multiplication by 10^k is required,
// sharing the same rounding guarantees.

// ryuFtoaFixed32 formats mant*(2^exp) with prec decimal digits.
func ryuFtoaFixed32(d *decimalSlice, mant uint32, exp int, prec int) {
	if prec < 0 {

    // [-512, 512], instead of [-32767, 32767].
    // For now this behavior is specific for SVDF, where 6 bits are reserved for
    // the reduce operation after element-wise multiplication between state and
    // time weights.
    if (tensor_property.number_of_bits == 10) {
      SmallVector<double, 4> mins(1, std::numeric_limits<double>::max());

      return true;
    }
  },
  /**
   * This strategy uses all 128 bits of {@link Hashing#murmur3_128} when hashing. It looks different
   * from the implementation in MURMUR128_MITZ_32 because we're avoiding the multiplication in the
   * loop and doing a (much simpler) += hash2. We're also changing the index to a positive number by
   * AND'ing with Long.MAX_VALUE instead of flipping the bits.
   */
  MURMUR128_MITZ_64() {
    @Override

	// invalid scalars and the zero value. BytesX returns an error for the point
	// at infinity, but in a prime order group such as the NIST curves that can
	// only be the result of a scalar multiplication if one of the inputs is the
	// zero scalar or the point at infinity.

	if boring.Enabled {
		return boring.ECDH(local.boring, remote.boring)
	}

	boring.Unreachable()

			desc: "negative uint",
			in: `go test fuzz v1
uint(-32)`,
			reject: true,
		},
		{
			desc: "int8 too large",
			in: `go test fuzz v1
int8(1234456)`,
			reject: true,
		},
		{
			desc: "multiplication in int value",
			in: `go test fuzz v1
int(20*5)`,
			reject: true,
		},
		{
			desc: "double negation",
			in: `go test fuzz v1
int(--5)`,
			reject: true,
		},
		{
			desc: "malformed bool",

const _ = maxInt + /* ERROR "constant addition overflow" */ 1
const _ = -maxInt - /* ERROR "constant subtraction overflow" */ 1
const _ = maxInt ^ /* ERROR "constant bitwise XOR overflow" */ -1
const _ = maxInt * /* ERROR "constant multiplication overflow" */ 2
const _ = maxInt << /* ERROR "constant shift overflow" */ 2
const _ = 1 << /* ERROR "constant shift overflow" */ prec

//      From step 1, we have
//         expm1(x) = either 2**k*[expm1(r)+1] - 1
//                  = or     2**k*[expm1(r) + (1-2**-k)]
//   4. Implementation notes:
//      (A). To save one multiplication, we scale the coefficient Qi
//           to Qi*2**i, and replace z by (x**2)/2.
//      (B). To achieve maximum accuracy, we compute expm1(x) by
//        (i)   if x < -56*ln2, return -1.0, (raise inexact if x!=inf)

      return true;
    }
  },
  /**
   * This strategy uses all 128 bits of {@link Hashing#murmur3_128} when hashing. It looks different
   * from the implementation in MURMUR128_MITZ_32 because we're avoiding the multiplication in the
   * loop and doing a (much simpler) += hash2. We're also changing the index to a positive number by
   * AND'ing with Long.MAX_VALUE instead of flipping the bits.
   */
  MURMUR128_MITZ_64() {
    @Override

    assertEquals(1, future.get(-1, SECONDS).intValue());
  }

  @J2ktIncompatible
  @GwtIncompatible // threads
  public void testOverflowTimeout() throws Exception {
    // First, sanity check that naive multiplication would really overflow to a negative number:
    long nanosPerSecond = NANOSECONDS.convert(1, SECONDS);
    assertThat(nanosPerSecond * Long.MAX_VALUE).isLessThan(0L);

}

def TF_XlaSparseDenseMatmulWithStaticBufferSizeOp : TF_Op<"XlaSparseDenseMatmulWithStaticBufferSize", [Pure]> {
  let summary = "A XLA op which performs the dense-sparse matrix multiplication.";

  let arguments = (ins
    TF_Int32Tensor:$row_pointers,
    TF_Int32Tensor:$sorted_sample_ids,
    TF_Int32Tensor:$sorted_token_ids,
    TF_Float32Tensor:$sorted_gains,