"strings"
	"text/scanner"

	"cmd/asm/internal/flags"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Input is the main input: a stack of readers and some macro definitions.
// It also handles #include processing (by pushing onto the input stack)
// and parses and instantiates macro definitions.
type Input struct {
	Stack
	includes        []string
	beginningOfLine bool
	ifdefStack      []bool

    sub: Optional[SubItem] = None
    if PYDANTIC_V2:
        model_config = {"json_schema_serialization_defaults_required": True}


def get_app_client(separate_input_output_schemas: bool = True) -> TestClient:
    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)

    @app.post("/items/")
    def create_item(item: Item):
        return item

    @app.post("/items-list/")

  private static void testReverse(float[] input, float[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Floats.reverse(input);
    assertThat(input).isEqualTo(expectedOutput);
  }

  private static void testReverse(
      float[] input, int fromIndex, int toIndex, float[] expectedOutput) {
    input = Arrays.copyOf(input, input.length);
    Floats.reverse(input, fromIndex, toIndex);

/**
 * A future whose value is derived from a collection of input futures.
 *
 * @param <InputT> the type of the individual inputs
 * @param <OutputT> the type of the output (i.e. this) future
 */
@GwtCompatible
@ElementTypesAreNonnullByDefault
abstract class AggregateFuture<InputT extends @Nullable Object, OutputT extends @Nullable Object>
    extends AggregateFutureState<OutputT> {

      : forward_inputs_(f_inputs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency

    for (int shift = 0; i < len; i++, shift += 8) {
      k1 ^= toInt(input[off + i]) << shift;
    }
    h1 ^= mixK1(k1);
    return fmix(h1, len);
  }

  private static int getIntLittleEndian(byte[] input, int offset) {
    return Ints.fromBytes(input[offset + 3], input[offset + 2], input[offset + 1], input[offset]);
  }

  private static int mixK1(int k1) {
    k1 *= C1;
    k1 = Integer.rotateLeft(k1, 15);

    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,
    std::unordered_map<const Node*, std::vector<int>>* input_nodes)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  input_tensors->reserve(ninputs);
  for (int i = 0; i < ninputs; ++i) {
    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(

    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);

      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];

   */
  HashCode hashBytes(byte[] input, int off, int len);

  /**
   * Shortcut for {@code newHasher().putBytes(input).hash()}. The implementation <i>might</i>
   * perform better than its longhand equivalent, but should not perform worse.
   *
   * @since 23.0
   */
  HashCode hashBytes(ByteBuffer input);

  /**
   * Shortcut for {@code newHasher().putUnencodedChars(input).hash()}. The implementation