code: # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for tf.data service server lib."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.data.experimental.service import server_lib
  
  from tensorflow.python.platform import test
  
  
  class ServerLibTest(test.TestCase):
  
    def testStartDispatcher(self):
      dispatcher = server_lib.DispatchServer(0, start=False)
      dispatcher.start()
  
    def testMultipleStartDispatcher(self):
      dispatcher = server_lib.DispatchServer(0, start=True)
      dispatcher.start()
  
    def testStartWorker(self):
      dispatcher = server_lib.DispatchServer(0)
      worker = server_lib.WorkerServer(0, dispatcher._address, start=False)
      worker.start()
  
    def testMultipleStartWorker(self):
      dispatcher = server_lib.DispatchServer(0)
      worker = server_lib.WorkerServer(0, dispatcher._address, start=True)
      worker.start()
  
    def testStopDispatcher(self):
      dispatcher = server_lib.DispatchServer(0)
      dispatcher._stop()
      dispatcher._stop()
  
    def testStopWorker(self):
      dispatcher = server_lib.DispatchServer(0)
      worker = server_lib.WorkerServer(0, dispatcher._address)
      worker._stop()
      worker._stop()
  
    def testStopStartDispatcher(self):
      dispatcher = server_lib.DispatchServer(0)
      dispatcher._stop()
      with self.assertRaisesRegex(
          RuntimeError, "Server cannot be started after it has been stopped"):
        dispatcher.start()
  
    def testStopStartWorker(self):
      dispatcher = server_lib.DispatchServer(0)
      worker = server_lib.WorkerServer(0, dispatcher._address)
      worker._stop()
      with self.assertRaisesRegex(
          RuntimeError, "Server cannot be started after it has been stopped"):
        worker.start()
  
    def testJoinDispatcher(self):
      dispatcher = server_lib.DispatchServer(0)
      dispatcher._stop()
      dispatcher.join()
  
    def testJoinWorker(self):
      dispatcher = server_lib.DispatchServer(0)
      worker = server_lib.WorkerServer(0, dispatcher._address)
      worker._stop()
      worker.join()
  
    def testDispatcherNumWorkers(self):
      dispatcher = server_lib.DispatchServer(0)
      self.assertEqual(0, dispatcher._num_workers())
      worker1 = server_lib.WorkerServer(0, dispatcher._address)  # pylint: disable=unused-variable
      self.assertEqual(1, dispatcher._num_workers())
      worker2 = server_lib.WorkerServer(0, dispatcher._address)  # pylint: disable=unused-variable
      self.assertEqual(2, dispatcher._num_workers())
  
  
  if __name__ == "__main__":
    test.main()
   # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  # RUN: (! %p/exported_python_args 2>&1) | FileCheck %s
  
  # pylint: disable=missing-docstring,line-too-long,dangerous-default-value
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow.compat.v2 as tf
  from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common
  
  
  class TestModule(tf.Module):
  
    @tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
    def some_function(self, x):
      return self.callee(x)
  
    # CHECK: While importing SavedModel function 'callee': in input signature:
    # CHECK-SAME: Unhandled structured value kind {{.*}} at index path: <value>.1.foo
    @tf.function
    def callee(self, x, n={'foo': 42}):
      return x
  
  
  if __name__ == '__main__':
    common.do_test(TestModule)
   # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for spectral_ops."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import itertools
  
  from absl.testing import parameterized
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.signal import spectral_ops
  from tensorflow.python.ops.signal import window_ops
  from tensorflow.python.platform import test
  
  
  @test_util.run_all_in_graph_and_eager_modes
  class SpectralOpsTest(test.TestCase, parameterized.TestCase):
  
    @staticmethod
    def _np_hann_periodic_window(length):
      if length == 1:
        return np.ones(1)
      odd = length % 2
      if not odd:
        length += 1
      window = 0.5 - 0.5 * np.cos(2.0 * np.pi * np.arange(length) / (length - 1))
      if not odd:
        window = window[:-1]
      return window
  
    @staticmethod
    def _np_frame(data, window_length, hop_length):
      num_frames = 1 + int(np.floor((len(data) - window_length) // hop_length))
      shape = (num_frames, window_length)
      strides = (data.strides[0] * hop_length, data.strides[0])
      return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
  
    @staticmethod
    def _np_stft(data, fft_length, hop_length, window_length):
      frames = SpectralOpsTest._np_frame(data, window_length, hop_length)
      window = SpectralOpsTest._np_hann_periodic_window(window_length)
      return np.fft.rfft(frames * window, fft_length)
  
    @staticmethod
    def _np_inverse_stft(stft, fft_length, hop_length, window_length):
      frames = np.fft.irfft(stft, fft_length)
      # Pad or truncate frames's inner dimension to window_length.
      frames = frames[..., :window_length]
      frames = np.pad(frames, [[0, 0]] * (frames.ndim - 1) +
                      [[0, max(0, window_length - frames.shape[-1])]], "constant")
      window = SpectralOpsTest._np_hann_periodic_window(window_length)
      return SpectralOpsTest._np_overlap_add(frames * window, hop_length)
  
    @staticmethod
    def _np_overlap_add(stft, hop_length):
      num_frames, window_length = np.shape(stft)
      # Output length will be one complete window, plus another hop_length's
      # worth of points for each additional window.
      output_length = window_length + (num_frames - 1) * hop_length
      output = np.zeros(output_length)
      for i in range(num_frames):
        output[i * hop_length:i * hop_length + window_length] += stft[i,]
      return output
  
    def _compare(self, signal, frame_length, frame_step, fft_length, tol):
      actual_stft = spectral_ops.stft(
          signal, frame_length, frame_step, fft_length, pad_end=False)
      signal_ph = array_ops.placeholder_with_default(signal, shape=signal.shape)
      actual_stft_from_ph = spectral_ops.stft(
          signal_ph, frame_length, frame_step, fft_length, pad_end=False)
  
      actual_inverse_stft = spectral_ops.inverse_stft(
          actual_stft, frame_length, frame_step, fft_length)
  
      actual_stft, actual_stft_from_ph, actual_inverse_stft = self.evaluate(
          [actual_stft, actual_stft_from_ph, actual_inverse_stft])
  
      actual_stft_ph = array_ops.placeholder_with_default(
          actual_stft, shape=actual_stft.shape)
      actual_inverse_stft_from_ph = self.evaluate(
          spectral_ops.inverse_stft(
              actual_stft_ph, frame_length, frame_step, fft_length))
  
      # Confirm that there is no difference in output when shape/rank is fully
      # unknown or known.
      self.assertAllClose(actual_stft, actual_stft_from_ph)
      self.assertAllClose(actual_inverse_stft, actual_inverse_stft_from_ph)
  
      expected_stft = SpectralOpsTest._np_stft(
          signal, fft_length, frame_step, frame_length)
      self.assertAllClose(expected_stft, actual_stft, rtol=tol, atol=tol)
  
      expected_inverse_stft = SpectralOpsTest._np_inverse_stft(
          expected_stft, fft_length, frame_step, frame_length)
      self.assertAllClose(
          expected_inverse_stft, actual_inverse_stft, rtol=tol, atol=tol)
  
    def test_shapes(self):
      signal = np.zeros((512,)).astype(np.float32)
  
      # If fft_length is not provided, the smallest enclosing power of 2 of
      # frame_length (8) is used.
      stft = spectral_ops.stft(signal, frame_length=7, frame_step=8,
                               pad_end=True)
      self.assertAllEqual([64, 5], stft.shape.as_list())
      self.assertAllEqual([64, 5], self.evaluate(stft).shape)
  
      stft = spectral_ops.stft(signal, frame_length=8, frame_step=8,
                               pad_end=True)
      self.assertAllEqual([64, 5], stft.shape.as_list())
      self.assertAllEqual([64, 5], self.evaluate(stft).shape)
  
      stft = spectral_ops.stft(signal, frame_length=8, frame_step=8,
                               fft_length=16, pad_end=True)
      self.assertAllEqual([64, 9], stft.shape.as_list())
      self.assertAllEqual([64, 9], self.evaluate(stft).shape)
  
      stft = spectral_ops.stft(signal, frame_length=16, frame_step=8,
                               fft_length=8, pad_end=True)
      self.assertAllEqual([64, 5], stft.shape.as_list())
      self.assertAllEqual([64, 5], self.evaluate(stft).shape)
  
      stft = np.zeros((32, 9)).astype(np.complex64)
  
      inverse_stft = spectral_ops.inverse_stft(stft, frame_length=8,
                                               fft_length=16, frame_step=8)
      expected_length = (stft.shape[0] - 1) * 8 + 8
      self.assertAllEqual([256], inverse_stft.shape.as_list())
      self.assertAllEqual([expected_length], self.evaluate(inverse_stft).shape)
  
    @parameterized.parameters(
        (512, 64, 32, 64, np.float32, 1e-4),
        (512, 64, 32, 64, np.float64, 1e-8),
        (512, 64, 64, 64, np.float32, 1e-4),
        (512, 64, 64, 64, np.float64, 1e-8),
        (512, 72, 64, 64, np.float32, 1e-4),
        (512, 72, 64, 64, np.float64, 1e-8),
        (512, 64, 25, 64, np.float32, 1e-4),
        (512, 64, 25, 64, np.float64, 1e-8),
        (512, 25, 15, 36, np.float32, 1e-4),
        (512, 25, 15, 36, np.float64, 1e-8),
        (123, 23, 5, 42, np.float32, 1e-4),
        (123, 23, 5, 42, np.float64, 1e-8))
    def test_stft_and_inverse_stft(self, signal_length, frame_length,
                                   frame_step, fft_length, np_rtype, tol):
      """Test that spectral_ops.stft/inverse_stft match a NumPy implementation."""
      signal = np.random.random(signal_length).astype(np_rtype)
      self._compare(signal, frame_length, frame_step, fft_length, tol)
  
    @parameterized.parameters(
        # 87.5% overlap.
        (4096, 256, 32, 256, np.float32, 1e-5, 1e-6),
        (4096, 256, 32, 256, np.float64, 1e-8, 1e-8),
        # 75% overlap.
        (4096, 256, 64, 256, np.float32, 1e-5, 1e-6),
        (4096, 256, 64, 256, np.float64, 1e-8, 1e-8),
        # Odd frame hop.
        (4096, 128, 25, 128, np.float32, 1e-3, 1e-6),
        (4096, 128, 25, 128, np.float64, 5e-4, 1e-8),
        # Odd frame length.
        (4096, 127, 32, 128, np.float32, 1e-3, 1e-6),
        (4096, 127, 32, 128, np.float64, 1e-3, 1e-8),
        # 50% overlap.
        (4096, 128, 64, 128, np.float32, 0.4, 1e-6),
        (4096, 128, 64, 128, np.float64, 0.4, 1e-8))
    def test_stft_round_trip(self, signal_length, frame_length, frame_step,
                             fft_length, np_rtype, threshold,
                             corrected_threshold):
      # Generate a random white Gaussian signal.
      signal = np.random.normal(size=signal_length).astype(np_rtype)
  
      stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length,
                               pad_end=False)
      inverse_stft = spectral_ops.inverse_stft(stft, frame_length, frame_step,
                                               fft_length)
      inverse_stft_corrected = spectral_ops.inverse_stft(
          stft, frame_length, frame_step, fft_length,
          window_fn=spectral_ops.inverse_stft_window_fn(frame_step))
      inverse_stft, inverse_stft_corrected = self.evaluate(
          [inverse_stft, inverse_stft_corrected])
  
      # Truncate signal to the size of inverse stft.
      signal = signal[:inverse_stft.shape[0]]
  
      # Ignore the frame_length samples at either edge.
      signal = signal[frame_length:-frame_length]
      inverse_stft = inverse_stft[frame_length:-frame_length]
      inverse_stft_corrected = inverse_stft_corrected[
          frame_length:-frame_length]
  
      # Check that the inverse and original signal are close up to a scale
      # factor.
      inverse_stft_scaled = inverse_stft / np.mean(np.abs(inverse_stft))
      signal_scaled = signal / np.mean(np.abs(signal))
      self.assertLess(np.std(inverse_stft_scaled - signal_scaled), threshold)
  
      # Check that the inverse with correction and original signal are close.
      self.assertLess(np.std(inverse_stft_corrected - signal),
                      corrected_threshold)
  
    @parameterized.parameters(
        (256, 32),
        (256, 64),
        (128, 25),
        (127, 32),
        (128, 64))
    def test_inverse_stft_window_fn(self, frame_length, frame_step):
      """Test that inverse_stft_window_fn has unit gain at each window phase."""
      hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32)
      inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
      inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32)
      hann_window, inverse_window = self.evaluate([hann_window, inverse_window])
  
      # Expect unit gain at each phase of the window.
      product_window = hann_window * inverse_window
      for i in range(frame_step):
        self.assertAllClose(1.0, np.sum(product_window[i::frame_step]))
  
    @parameterized.parameters((256, 64), (128, 32))
    def test_inverse_stft_window_fn_special_case(self, frame_length, frame_step):
      """Test inverse_stft_window_fn in special overlap = 3/4 case."""
      # Cases in which frame_length is an integer multiple of 4 * frame_step are
      # special because they allow exact reproduction of the waveform with a
      # squared Hann window (Hann window in both forward and reverse transforms).
      # In the case where frame_length = 4 * frame_step, that combination
      # produces a constant gain of 1.5, and so the corrected window will be the
      # Hann window / 1.5.
      hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32)
      inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
      inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32)
      self.assertAllClose(hann_window, inverse_window * 1.5)
  
    @staticmethod
    def _compute_stft_gradient(signal, frame_length=32, frame_step=16,
                               fft_length=32):
      """Computes the gradient of the STFT with respect to `signal`."""
      stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length)
      magnitude_stft = math_ops.abs(stft)
      loss = math_ops.reduce_sum(magnitude_stft)
      return gradients_impl.gradients([loss], [signal])[0]
  
    def test_gradients(self):
      """Test that spectral_ops.stft has a working gradient."""
      # TODO(rjryan): Update gradient tests for Eager.
      if context.executing_eagerly():
        return
      with self.session(use_gpu=True) as sess:
        signal_length = 512
  
        # An all-zero signal has all zero gradients with respect to the sum of the
        # magnitude STFT.
        empty_signal = array_ops.zeros([signal_length], dtype=dtypes.float32)
        empty_signal_gradient = sess.run(
            self._compute_stft_gradient(empty_signal))
        self.assertTrue((empty_signal_gradient == 0.0).all())
  
        # A sinusoid will have non-zero components of its gradient with respect to
        # the sum of the magnitude STFT.
        sinusoid = math_ops.sin(
            2 * np.pi * math_ops.linspace(0.0, 1.0, signal_length))
        sinusoid_gradient = self.evaluate(self._compute_stft_gradient(sinusoid))
        self.assertFalse((sinusoid_gradient == 0.0).all())
  
    @parameterized.parameters(
        (64, 16, 8, 16, np.float32, 2e-3, 5e-4),
        (64, 16, 8, 16, np.float64, 1e-8, 1e-8),
        (64, 16, 16, 16, np.float32, 2e-3, 5e-4),
        (64, 16, 16, 16, np.float64, 1e-8, 1e-8),
        (64, 16, 7, 16, np.float32, 2e-3, 5e-4),
        (64, 16, 7, 16, np.float64, 1e-8, 1e-8),
        (64, 7, 4, 9, np.float32, 2e-3, 5e-4),
        (64, 7, 4, 9, np.float64, 1e-8, 1e-8),
        (29, 5, 1, 10, np.float32, 2e-3, 5e-4),
        (29, 5, 1, 10, np.float64, 1e-8, 1e-8))
    def test_gradients_numerical(self, signal_length, frame_length, frame_step,
                                 fft_length, np_rtype, forward_tol, backward_tol):
      # TODO(rjryan): Investigate why STFT gradient error is so high.
      signal = np.random.rand(signal_length).astype(np_rtype) * 2 - 1
  
      def forward(signal):
        return spectral_ops.stft(
            signal, frame_length, frame_step, fft_length, pad_end=False)
      ((f_jacob_t,), (f_jacob_n,)) = gradient_checker_v2.compute_gradient(
          forward, [signal])
      self.assertAllClose(f_jacob_t, f_jacob_n,
                          rtol=forward_tol, atol=forward_tol)
  
      def backward(stft):
        return spectral_ops.inverse_stft(
            stft, frame_length, frame_step, fft_length)
  
      stft = forward(signal)
      ((b_jacob_t,), (b_jacob_n,)) = gradient_checker_v2.compute_gradient(
          backward, [stft])
      self.assertAllClose(b_jacob_t, b_jacob_n,
                          rtol=backward_tol, atol=backward_tol)
  
    @parameterized.parameters(
        itertools.product(
            (4000,),
            (256,),
            (np.float32, np.float64),
            ("ortho", None),
            ("vorbis", "kaiser_bessel_derived", None),
            (False, True)))
    def test_mdct_round_trip(self, signal_length, frame_length, np_rtype,
                             norm, window_type, pad_end):
      if np_rtype == np.float32:
        tol = 1e-5
      else:
        if window_type == "kaiser_bessel_derived":
          tol = 1e-6
        else:
          tol = 1e-8
      # Generate a random white Gaussian signal.
      signal = np.random.normal(size=signal_length).astype(np_rtype)
      if window_type == "vorbis":
        window_fn = window_ops.vorbis_window
      elif window_type == "kaiser_bessel_derived":
        window_fn = window_ops.kaiser_bessel_derived_window
      elif window_type is None:
        window_fn = None
      mdct = spectral_ops.mdct(signal, frame_length, norm=norm,
                               window_fn=window_fn, pad_end=pad_end)
      inverse_mdct = spectral_ops.inverse_mdct(mdct, norm=norm,
                                               window_fn=window_fn)
      inverse_mdct = self.evaluate(inverse_mdct)
  
      # Truncate signal and inverse_mdct to their minimum length.
      min_length = np.minimum(signal.shape[0], inverse_mdct.shape[0])
      # Ignore the half_len samples at either edge.
      half_len = frame_length // 2
      signal = signal[half_len:min_length-half_len]
      inverse_mdct = inverse_mdct[half_len:min_length-half_len]
  
      # Check that the inverse and original signal are close.
      self.assertAllClose(inverse_mdct, signal, atol=tol, rtol=tol)
  
  if __name__ == "__main__":
    test.main()
  
apis: tensorflow.python.data.experimental.service.server_lib.DispatchServer tensorflow.python.platform.test.main tensorflow.python.data.experimental.service.server_lib.WorkerServer
apis: tensorflow.compat.v2.TensorSpec tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model.common.do_test
apis: numpy.ones numpy.sum tensorflow.python.ops.signal.window_ops.hann_window tensorflow.python.ops.array_ops.placeholder_with_default numpy.fft.irfft tensorflow.python.ops.signal.spectral_ops.inverse_stft_window_fn tensorflow.python.ops.gradients_impl.gradients tensorflow.python.ops.gradient_checker_v2.compute_gradient numpy.abs numpy.random.rand tensorflow.python.ops.math_ops.linspace numpy.fft.rfft numpy.minimum numpy.zeros tensorflow.python.ops.signal.spectral_ops.stft tensorflow.python.ops.signal.spectral_ops.inverse_mdct tensorflow.python.ops.signal.spectral_ops.inverse_stft tensorflow.python.ops.math_ops.reduce_sum tensorflow.python.ops.array_ops.zeros tensorflow.python.ops.signal.spectral_ops.mdct numpy.lib.stride_tricks.as_strided numpy.arange numpy.std tensorflow.python.ops.math_ops.abs numpy.random.random tensorflow.python.platform.test.main numpy.shape numpy.random.normal tensorflow.python.eager.context.executing_eagerly