tensorflow/tensorflow

# Copyright 2021 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
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# ==============================================================================
"""Test configs for random_standard_normal."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.lite.testing.zip_test_utils import create_tensor_data
from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
from tensorflow.lite.testing.zip_test_utils import register_make_test_function


@register_make_test_function()
def make_random_standard_normal_tests(options):
  """Make a set of tests to do random_standard_normal."""

  test_parameters = [{
      "input_shape": [[1]],
      "input_dtype": [tf.int32],
      "shape": [[10]],
      "seed": [None, 0, 1234],
      "seed2": [0, 5678],
      "dtype": [tf.float32],
  }, {
      "input_shape": [[3]],
      "input_dtype": [tf.int32],
      "shape": [[2, 3, 4]],
      "seed": [0, 1234],
      "seed2": [None, 0, 5678],
      "dtype": [tf.float32],
  }]

  def build_graph(parameters):
    """Build the op testing graph."""
    tf.compat.v1.set_random_seed(seed=parameters["seed"])
    input_value = tf.compat.v1.placeholder(
        name="shape",
        shape=parameters["input_shape"],
        dtype=parameters["input_dtype"])
    out = tf.random.normal(
        shape=input_value, dtype=parameters["dtype"], seed=parameters["seed2"])
    return [input_value], [out]

  def build_inputs(parameters, sess, inputs, outputs):
    input_value = create_tensor_data(
        parameters["input_dtype"],
        parameters["input_shape"],
        min_value=1,
        max_value=10)
    return [input_value], sess.run(
        outputs, feed_dict=dict(zip(inputs, [input_value])))

  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)