"//tensorflow/cc/experimental/libtf:__pkg__",
        "//tensorflow/cc/experimental/libtf:__subpackages__",
        # copybara:uncomment_begin(google-only)
        # "//tensorflow/cc/experimental/tf2:__pkg__",
        # "//tensorflow/cc/experimental/tf2:__subpackages__",
        # copybara:uncomment_end
        "//tensorflow/compiler/mlir/tensorflow/c:__subpackages__",
        "//tensorflow/core/transforms:__subpackages__",
    ],

limitations under the License.
==============================================================================*/

// This pass separates SparseCore, TensorCore, and non-TPU operations into
// separate functions for proper sequencing of TF2 TPU Embedding (see
// tpu_embedding_v3.py). This pass is a precursor for pipelining (see
// embedding_pipelining.cc) and DOES NOT permit parallel execution across SC and

_CalibrationMethod = qc.CalibrationOptions.CalibrationMethod


# Test cases for Static Range Quantization.
# Tries to run all tests cases in both the graph mode (default in TF1) and the
# eager mode (default in TF2) to ensure support for when TF2 is disabled.
class StaticRangeQuantizationTest(quantize_model_test_base.QuantizedModelTest):

  @parameterized.parameters(
      testing.parameter_combinations([{
          'bias_fn': (
              None,

  return *jitrt_flags;
}

ConfigProto::Experimental::MlirBridgeRollout GetMlirBridgeRolloutState(
    std::optional<const ConfigProto> config_proto) {
  // TF1 graphs that do not override Sessions's ConfigProto and TF2 graphs
  // can enable/disable the graph via tf_mlir_enable_mlir_bridge.
  auto tf_mlir_enable_mlir_bridge =
      GetMlirCommonFlags()->tf_mlir_enable_mlir_bridge;
  if (tf_mlir_enable_mlir_bridge !=

                          status.ToString())
      .IgnoreError();

  return status;
}

// V1 Compat Bridge takes a TF Executor dialect and extracts the TF2 portion
// and inserts it into a submodule. We just want to run the clustering
// portion of the pipeline on just the single submodule.
absl::Status RunClusteringPipelineOnSubmodule(

    representative_dataset: rd.RepresentativeDataset,
) -> None:
  """Runs the representative dataset through a function for calibration.

  NOTE: This is intended to be run in eager mode (TF2).

  Args:
    func: The function to run the representative samples through.
    representative_dataset: Representative dataset used for calibration. The

        "//tensorflow/core:tensorflow",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core:testlib",
    ],
)

# A subset of the TF2 saved models can be generated with this tool.
py_strict_binary(
    name = "testdata/generate_saved_models",
    srcs = ["testdata/generate_saved_models.py"],
    data = [
        ":saved_model_asset_data",

  """Test cases regarding the use of QuantizationOptions proto.

  Run all tests cases in both the graph mode (default in TF1) and the eager mode
  (default in TF2) to ensure support for when TF2 is disabled.
  """

  class SimpleModel(module.Module):

    def __init__(self):
      self.filters = np.random.uniform(low=-1.0, high=1.0, size=(4, 3)).astype(
          'f4'
      )

# RESULT1: name: "Identity"
# RESULT1-NEXT: quantization:
# RESULT1-NEXT: scale: [ 0.007523 ],
# RESULT1-NEXT: zero_point: [ 116 ]

# TODO  Actually RESULT1 represents in incomplete implementation
# Currently TF2.2.0-rc3 all but the first fake_quant in 
# sequence are dropped.  A correct transalation would retain the second as a requantization
# op.

# CORRECT1:name: "Identity"
# CORRECT1-NEXT: quantization:

    extra result of type `!tf_executor.control` is always produced by every
    `tf_executor.island`.
    Within an island, execution semantics follow standard sequential behavior as
    expected by TF2 and by compiler analyses and transformations, and values
    can’t be dead. Other nested `tf_executor.graph` operations can be present in
    the region to re-enable the TensorFlow executor for a subsection of the
    code.