srcs = ["calibration_algorithm.py"],
    deps = [
        ":calibration_statistics_proto_py",
        "//tensorflow/compiler/mlir/quantization/stablehlo:quantization_config_proto_py",
        "//third_party/py/numpy",
    ],
)

pytype_strict_contrib_test(
    name = "calibration_algorithm_test",
    srcs = ["calibration_algorithm_test.py"],
    deps = [
        ":calibration_algorithm",

        return success();
      }
      // Insert tranposes to support scatter operations generated from
      // numpy-like slice operations:
      //   nd_array[:, [i,j]] = [i_values, j_values]
      //
      if (scatter_dims_to_operand_dims != inserted_window_dims) {
        // Support only dimension numbers generated by numpy-like slice
        // operations.
        return rewriter.notifyMatchFailure(

from tensorflow.python.platform import test


class TestOpsDefsTest(test_utils.OpsDefsTest):

  def test_test_ops(self):
    attr = tf.function(test_ops.test_attr)(tf.float32)
    self.assertAllClose(attr.numpy(), 100.0)


if __name__ == '__main__':
  os.environ['TF_MLIR_TFR_LIB_DIR'] = (
      'tensorflow/compiler/mlir/tfr/examples/customization')

  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {

      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |

# limitations under the License.
# ==============================================================================
"""Concurrency tests for quantize_model."""

from concurrent import futures

import numpy as np
import tensorflow  # pylint: disable=unused-import

from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2 as quant_opts_pb2

large nodes for Merger::Read and Merger::Merge.
"""

from collections.abc import Sequence

import os

from absl import app
from absl import flags
import numpy as np

from tensorflow.python.compat import v2_compat
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.lib.io import file_io

# limitations under the License.
# ==============================================================================
"""Tests for CalibrationAlgorithm."""
from absl.testing import parameterized
import numpy as np

from tensorflow.compiler.mlir.quantization.stablehlo import quantization_config_pb2 as stablehlo_quant_config_pb2
from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_algorithm

#     ],
#     deps = [
#         "//third_party/py/mlir:ir",
#         "//third_party/py/mlir:stablehlo_dialect",
#         "//third_party/py/mlir/_mlir_libs:_mlirRegisterEverything",
#         "//third_party/py/numpy",
#         "//tensorflow:tensorflow_py",
#         "//tensorflow/compiler/mlir/stablehlo",
#         "//tensorflow/python/eager:def_function",
#         "//tensorflow/python/framework:dtypes",

// Op: Relu()
// Summary: Computes rectified linear: `max(features, 0)`.
//
// Description:
//   See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
//   Example usage:
//   >>> tf.nn.relu([-2., 0., 3.]).numpy()
//   array([0., 0., 3.], dtype=float32)
Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features,
            AbstractTensorHandle** activations, const char* name,
            const char* raw_device_name) {