# 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 transpose."""
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_transpose_tests(options):
  """Make a set of tests to do transpose."""

  # TODO(nupurgarg): Add test for uint8.
  test_parameters = [{
      "dtype": [tf.int32, tf.int64, tf.float32],
      "input_shape": [[2, 2, 3]],
      "perm": [[0, 1, 2], [0, 2, 1]],
      "constant_perm": [True, False],
      "fully_quantize": [False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[1, 2, 3, 4]],
      "perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
      "constant_perm": [True, False],
      "fully_quantize": [False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[1, 2, 3, 4, 5]],
      "perm": [[4, 3, 2, 1, 0]],
      "constant_perm": [True, False],
      "fully_quantize": [False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[2, 2, 3]],
      "perm": [[0, 1, 2], [0, 2, 1]],
      "constant_perm": [True],
      "fully_quantize": [True],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[1, 2, 3, 4]],
      "perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
      "constant_perm": [True],
      "fully_quantize": [True],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[1, 2, 3, 4, 5]],
      "perm": [[0, 1, 2, 3, 4], [3, 4, 0, 1, 2]],
      "constant_perm": [True],
      "fully_quantize": [True, False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[2, 2]],
      "perm": [[-2, -1]],
      "constant_perm": [True, False],
      "fully_quantize": [False],
  }]

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

    if parameters["constant_perm"]:
      perm = parameters["perm"]
      input_tensors = [input_tensor]
    else:
      shape = len(parameters["perm"])
      perm = tf.compat.v1.placeholder(dtype=tf.int32, name="perm", shape=shape)
      input_tensors = [input_tensor, perm]

    out = tf.transpose(a=input_tensor, perm=perm)
    return input_tensors, [out]

  def build_inputs(parameters, sess, inputs, outputs):
    values = [
        create_tensor_data(parameters["dtype"], parameters["input_shape"],
                           min_value=-1, max_value=1)
    ]
    if not parameters["constant_perm"]:
      values.append(np.array(parameters["perm"]))
    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))

  make_zip_of_tests(
      options,
      test_parameters,
      build_graph,
      build_inputs)