# 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 fully_connected."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow.compat.v1 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_fully_connected_tests(options):
  """Make a set of tests to do fully_connected."""

  test_parameters = [{
      "shape1": [[3, 3]],
      "shape2": [[3, 3]],
      "transpose_a": [True, False],
      "transpose_b": [True, False],
      "constant_filter": [True, False],
      "fully_quantize": [False],
  }, {
      "shape1": [[4, 4], [1, 4], [4]],
      "shape2": [[4, 4], [4, 1], [4]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True, False],
      "fully_quantize": [False],
  }, {
      "shape1": [[40, 37]],
      "shape2": [[37, 40]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True, False],
      "fully_quantize": [False],
  }, {
      "shape1": [[40, 37]],
      "shape2": [[40, 37]],
      "transpose_a": [False],
      "transpose_b": [True],
      "constant_filter": [True, False],
      "fully_quantize": [False],
  }, {
      "shape1": [[5, 3]],
      "shape2": [[5, 3]],
      "transpose_a": [True],
      "transpose_b": [False],
      "constant_filter": [True, False],
      "fully_quantize": [False],
  }, {
      "shape1": [[1, 3]],
      "shape2": [[3, 3]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True],
      "fully_quantize": [True],
  }, {
      "shape1": [[1, 4], [4]],
      "shape2": [[4, 4], [4, 1], [4]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True],
      "fully_quantize": [True],
  }, {
      "shape1": [[1, 37], [2, 37]],
      "shape2": [[37, 40]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True],
      "fully_quantize": [True],
  }, {
      "shape1": [[1, 3], [2, 3]],
      "shape2": [[3, 5], [3, 1]],
      "transpose_a": [False],
      "transpose_b": [False],
      "constant_filter": [True],
      "fully_quantize": [True],
  }]

  def build_graph(parameters):
    """Build a matmul graph given `parameters`."""
    input_tensor1 = tf.compat.v1.placeholder(
        dtype=tf.float32, name="input1", shape=parameters["shape1"])

    # Get input_tensor2 either as a placeholder or constants. Also get a list of
    # the input tensors that are represented as placeholders.
    if parameters["constant_filter"]:
      input_tensor2 = create_tensor_data(
          np.float32, parameters["shape2"], min_value=-1, max_value=1)
      input_tensors = [input_tensor1]
    else:
      input_tensor2 = tf.compat.v1.placeholder(
          dtype=tf.float32, name="input2", shape=parameters["shape2"])
      input_tensors = [input_tensor1, input_tensor2]

    out = tf.matmul(
        input_tensor1,
        input_tensor2,
        transpose_a=parameters["transpose_a"],
        transpose_b=parameters["transpose_b"])
    return input_tensors, [out]

  def build_inputs(parameters, sess, inputs, outputs):
    # pylint: disable=g-doc-return-or-yield, g-doc-args
    """Build list of input values.

    It either contains 1 tensor (input_values1) or
    2 tensors (input_values1, input_values2) based on whether the second input
    is a constant or variable input.
    """

    values = [
        create_tensor_data(
            np.float32, shape=parameters["shape1"], min_value=-1, max_value=1)
    ]
    if not parameters["constant_filter"]:
      values.append(
          create_tensor_data(
              np.float32, parameters["shape2"], min_value=-1, max_value=1))
    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))

  make_zip_of_tests(
      options,
      test_parameters,
      build_graph,
      build_inputs,
      expected_tf_failures=14)
