# 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.
# ==============================================================================
"""Test configs for conv2d_transpose."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
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_conv2d_transpose_tests(options):
  """Make a set of tests to do transpose_conv."""

  test_parameters = [{
      "input_shape": [[1, 50, 54, 3]],
      "filter_shape": [[1, 1, 8, 3], [1, 2, 8, 3], [1, 3, 8, 3], [1, 4, 8, 3]],
      "output_shape": [[1, 100, 108, 8]],
      "dynamic_output_shape": [True, False],
  }, {
      "input_shape": [[1, 16, 1, 512]],
      "filter_shape": [[4, 1, 512, 512]],
      "output_shape": [[1, 32, 1, 512]],
      "dynamic_output_shape": [True, False],
  }, {
      "input_shape": [[1, 128, 128, 1]],
      "filter_shape": [[4, 4, 1, 1]],
      "output_shape": [[1, 256, 256, 1]],
      "dynamic_output_shape": [True, False],
  }]

  def build_graph(parameters):
    """Build a transpose_conv graph given `parameters`."""
    input_tensor = tf.compat.v1.placeholder(
        dtype=tf.float32, name="input", shape=parameters["input_shape"])

    filter_tensor = tf.compat.v1.placeholder(
        dtype=tf.float32, name="filter", shape=parameters["filter_shape"])

    input_tensors = [input_tensor, filter_tensor]

    if parameters["dynamic_output_shape"]:
      output_shape = tf.compat.v1.placeholder(dtype=tf.int32, shape=[4])
      input_tensors.append(output_shape)
    else:
      output_shape = parameters["output_shape"]

    out = tf.nn.conv2d_transpose(
        input_tensor,
        filter_tensor,
        output_shape=output_shape,
        padding="SAME",
        strides=(1, 2, 2, 1))

    return input_tensors, [out]

  def build_inputs(parameters, sess, inputs, outputs):
    values = [
        create_tensor_data(np.float32, parameters["input_shape"]),
        create_tensor_data(np.float32, parameters["filter_shape"])
    ]
    if parameters["dynamic_output_shape"]:
      values.append(np.array(parameters["output_shape"]))

    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))

  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
