_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024


def _is_qat_saved_model(saved_model_path: str):
  """Checks if the SavedModel is QAT-enabled by looking for 'FakeQuant' ops."""
  saved_model_proto = saved_model_loader.parse_saved_model(saved_model_path)
  for meta_graph in saved_model_proto.meta_graphs:
    if any(
        node.op.startswith('FakeQuant') for node in meta_graph.graph_def.node

    """
    for _ in range(num_examples):
      yield {input_key: random_ops.random_uniform(shape, minval, maxval, dtype)}

  def _save_tf1_model(
      self,
      sess: session.Session,
      saved_model_path: str,
      signature_key: str,
      tags: Collection[str],
      inputs: Mapping[str, core.Tensor],
      outputs: Mapping[str, core.Tensor],
      init_op: Optional[ops.Operation] = None,

_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]


def get_signatures_from_saved_model(
    saved_model_path: str,
    signature_keys: Optional[Sequence[str]] = None,
    tags: Optional[Collection[str]] = None,
) -> Dict[str, meta_graph_pb2.SignatureDef]:
  """Gets a map from signature keys to their SignatureDef.

  Args:
    saved_model_path: Path to the saved model.

    # (specified by `kDefaultConstantSizeThresholdInBytes`) for unfreezing.
    self._create_matmul_model(
        input_shape=(1, 20),
        weight_shape=(20, 4096),
        saved_model_path=self._input_saved_model_path,
    )

    repr_ds = self._create_data_generator(
        input_key='input_tensor', shape=(1, 20), num_examples=2
    )

    tags = {tag_constants.SERVING}

      the function matching the signature key.

      Raises `StatusNotOk` exception if when the run was unsuccessful.
      )pbdoc",
      py::arg("saved_model_path"), py::arg("dst_saved_model_path"),
      py::arg("quantization_options_serialized"), py::kw_only(),
      py::arg("signature_keys"), py::arg("signature_def_map_serialized"),
      py::arg("py_function_library"),

        ),
        1 / 3,
    )

  def test_when_preset_not_srq_raises_error(self):
    self._create_matmul_model(
        input_shape=(1, 1024),
        weight_shape=(1024, 3),
        saved_model_path=self._input_saved_model_path,
    )

    config = qc.QuantizationConfig()
    with self.assertRaisesRegex(ValueError, 'only supports static-range PTQ'):
      quantization.quantize_saved_model(