chore: import upstream snapshot with attribution

tensorflow/lite/python/kernel_tests/signal/BUILD

@@ -0,0 +1,38 @@
+load("@xla//third_party/rules_python/python:py_library.bzl", "py_library")
+load("//tensorflow:tensorflow.default.bzl", "cuda_py_strict_test")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    licenses = ["notice"],
+)
+
+py_library(
+    name = "test_util",
+    srcs = ["test_util.py"],
+    strict_deps = True,
+    deps = [
+        "//tensorflow/lite/python:interpreter",
+        "//tensorflow/lite/python:lite",
+        "//tensorflow/python/eager:def_function",
+    ],
+)
+
+cuda_py_strict_test(
+    name = "window_ops_test",
+    srcs = ["window_ops_test.py"],
+    shard_count = 4,
+    tags = [
+        "no_rocm",
+        "no_windows_gpu",
+    ],
+    deps = [
+        ":test_util",
+        "//tensorflow/python/framework:for_generated_wrappers",
+        "//tensorflow/python/framework:tensor_spec",
+        "//tensorflow/python/framework:test_lib",
+        "//tensorflow/python/ops/signal:window_ops",
+        "//tensorflow/python/platform:client_testlib",
+        "//third_party/py/numpy",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)

tensorflow/lite/python/kernel_tests/signal/test_util.py

@@ -0,0 +1,69 @@
+# 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.
+# ==============================================================================
+"""Test utilities for tf.signal."""
+
+from tensorflow.lite.python import interpreter
+from tensorflow.lite.python import lite
+from tensorflow.python.eager import def_function
+
+
+def tflite_convert(fn, input_templates):
+  """Converts the provided fn to tf.lite model.
+
+  Args:
+    fn: A callable that expects a list of inputs like input_templates that
+      returns a tensor or structure of tensors.
+    input_templates: A list of Tensors, ndarrays or TensorSpecs describing the
+      inputs that fn expects. The actual values of the Tensors or ndarrays are
+      unused.
+
+  Returns:
+    The serialized tf.lite model.
+  """
+  fn = def_function.function(fn)
+  concrete_func = fn.get_concrete_function(*input_templates)
+  converter = lite.TFLiteConverterV2([concrete_func])
+  return converter.convert()
+
+
+def evaluate_tflite_model(tflite_model, input_ndarrays):
+  """Evaluates the provided tf.lite model with the given input ndarrays.
+
+  Args:
+    tflite_model: bytes. The serialized tf.lite model.
+    input_ndarrays: A list of NumPy arrays to feed as input to the model.
+
+  Returns:
+    A list of ndarrays produced by the model.
+
+  Raises:
+    ValueError: If the number of input arrays does not match the number of
+      inputs the model expects.
+  """
+  the_interpreter = interpreter.Interpreter(model_content=tflite_model)
+  the_interpreter.allocate_tensors()
+
+  input_details = the_interpreter.get_input_details()
+  output_details = the_interpreter.get_output_details()
+
+  if len(input_details) != len(input_ndarrays):
+    raise ValueError('Wrong number of inputs: provided=%s, '
+                     'input_details=%s output_details=%s' % (
+                         input_ndarrays, input_details, output_details))
+  for input_tensor, data in zip(input_details, input_ndarrays):
+    the_interpreter.set_tensor(input_tensor['index'], data)
+  the_interpreter.invoke()
+  return [the_interpreter.get_tensor(details['index'])
+          for details in output_details]

tensorflow/lite/python/kernel_tests/signal/window_ops_test.py

@@ -0,0 +1,60 @@
+# 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 window_ops."""
+
+from absl.testing import parameterized
+import numpy as np
+
+from tensorflow.lite.python.kernel_tests.signal import test_util
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import tensor_spec
+from tensorflow.python.framework import test_util as tf_test_util
+from tensorflow.python.ops.signal import window_ops
+from tensorflow.python.platform import test
+
+
+@tf_test_util.run_all_in_graph_and_eager_modes
+class WindowOpsTest(test.TestCase, parameterized.TestCase):
+
+  @parameterized.parameters(
+      # Only float32 is supported.
+      (window_ops.hann_window, 10, False, dtypes.float32),
+      (window_ops.hann_window, 10, True, dtypes.float32),
+      (window_ops.hamming_window, 10, False, dtypes.float32),
+      (window_ops.hamming_window, 10, True, dtypes.float32),
+      (window_ops.vorbis_window, 12, None, dtypes.float32),
+  )
+  def test_tflite_convert(self, window_fn, window_length, periodic, dtype):
+
+    def fn(window_length):
+      try:
+        return window_fn(window_length, periodic=periodic, dtype=dtype)
+      except TypeError:
+        return window_fn(window_length, dtype=dtype)
+
+    tflite_model = test_util.tflite_convert(
+        fn, [tensor_spec.TensorSpec(shape=[], dtype=dtypes.int32)]
+    )
+    window_length = np.array(window_length).astype(np.int32)
+    (actual_output,) = test_util.evaluate_tflite_model(
+        tflite_model, [window_length]
+    )
+
+    expected_output = self.evaluate(fn(window_length))
+    self.assertAllClose(actual_output, expected_output, rtol=1e-6, atol=1e-6)
+
+
+if __name__ == '__main__':
+  test.main()