# 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 reduce operators."""
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


def make_reduce_tests(reduce_op,
                      min_value=-10,
                      max_value=10,
                      boolean_tensor_only=False,
                      allow_fully_quantize=False):
  """Make a set of tests to do reduce operation.

  Args:
    reduce_op: TensorFlow reduce operation to test, i.e. `tf.reduce_mean`.
    min_value: min value for created tensor data.
    max_value: max value for created tensor data.
    boolean_tensor_only: If true, will only generate tensor with boolean value.
    allow_fully_quantize: bool, whether fully_quantize is allowed.

  Returns:
    a function representing the true generator with `reduce_op_in` curried.
  """

  def f(options):
    """Actual function that generates examples."""

    test_parameters = [
        {
            "input_dtype": [tf.float32, tf.int32, tf.int64],
            "input_shape": [[3, 3, 2, 4]],
            "axis": [
                0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0],
                [2, 1], [2, 1, 0], [2, 0, 1], -1, -2, -3, [1, -1], [0, -1],
                [-1, 0], [-1, -2, -3], [0, 0, 0], [2, 2, 0], [1, 0, -3, -3]
            ],
            "const_axis": [True, False],
            "keepdims": [True, False],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape": [[1, 8, 8, 3]],
            "axis": [
                0, 1, 2, 3, [1, 2], [0, 3], [1, 2, 3], [0, 1, 2,
                                                        3], [3, 2, 1, 0],
                [3, 1, 0, 2], [2, 0], [3, 0], [3, 1], [1, 0], -1, -2, -3, -4,
                [0, -2], [2, 3, -1, 0], [3, 1, 2, -3], [3, -4], [2, 2, 2],
                [2, 2, 3], [-3, -3, -4], [-3, 2, 1]
            ],
            "const_axis": [True, False],
            "keepdims": [True, False],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape": [[], [1, 8, 8, 3], [3, 2, 4]],
            "axis": [[]],  # shape is: [0]
            "const_axis": [False],
            "keepdims": [True, False],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape": [[], [1, 8, 8, 3], [3, 2, 4]],
            "axis": [None],  # shape is: []
            "const_axis": [True],
            "keepdims": [True, False],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape": [[3, 3, 2, 4]],
            "axis": [
                0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0],
                [2, 1], [2, 1, 0], [2, 0, 1], -1, -2, -3, [1, -1], [0, -1],
                [-1, 0], [-1, -2, -3], [0, 0, 0], [2, 2, 0], [1, 0, -3, -3]
            ],
            "const_axis": [True],
            "keepdims": [True, False],
            "fully_quantize": [True],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape": [[1, 8, 8, 4], [1, 8, 8, 3]],
            "axis": [
                0, 1, 2, 3, [0], [1], [2], [3], [-1], [-2], [-3], [1, 2],
                [0, 3], [1, 2, 3], [1, 3], [2, 3]
            ],
            "const_axis": [True],
            "keepdims": [True, False],
            "fully_quantize": [True],
        },
    ]
    # test_parameters include fully_quantize option only when
    # allow_fully_quantize is True.
    if not allow_fully_quantize:
      test_parameters = [
          test_parameter for test_parameter in test_parameters
          if True not in test_parameter["fully_quantize"]
      ]

    def build_graph(parameters):
      """Build the mean op testing graph."""
      dtype = parameters["input_dtype"]
      if boolean_tensor_only:
        dtype = tf.bool
      input_tensor = tf.compat.v1.placeholder(
          dtype=dtype, name="input", shape=parameters["input_shape"])

      # Get axis as either a placeholder or constants.
      if parameters["const_axis"]:
        axis = parameters["axis"]
        input_tensors = [input_tensor]
      else:
        if isinstance(parameters["axis"], list):
          shape = [len(parameters["axis"])]
        else:
          shape = []  # shape for None or integers.
        axis = tf.compat.v1.placeholder(
            dtype=tf.int32, name="axis", shape=shape)
        input_tensors = [input_tensor, axis]

      out = reduce_op(input_tensor, axis=axis, keepdims=parameters["keepdims"])
      return input_tensors, [out]

    def build_inputs(parameters, sess, inputs, outputs):
      """Build the inputs for reduced operators."""

      dtype = parameters["input_dtype"]
      if boolean_tensor_only:
        dtype = tf.bool
      values = [
          create_tensor_data(
              dtype,
              parameters["input_shape"],
              min_value=min_value,
              max_value=max_value)
      ]
      if not parameters["const_axis"]:
        values.append(np.array(parameters["axis"]))
      return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))

    make_zip_of_tests(options, test_parameters, build_graph, build_inputs)

  return f


@register_make_test_function()
def make_mean_tests(options):
  """Make a set of tests to do mean."""
  return make_reduce_tests(
      tf.reduce_mean,
      min_value=-1,
      max_value=1,
      boolean_tensor_only=False,
      allow_fully_quantize=True)(
          options)


@register_make_test_function()
def make_sum_tests(options):
  """Make a set of tests to do sum."""
  return make_reduce_tests(
      tf.reduce_sum,
      min_value=-1,
      max_value=1,
      boolean_tensor_only=False,
      allow_fully_quantize=True)(
          options)


@register_make_test_function()
def make_reduce_prod_tests(options):
  """Make a set of tests to do prod."""
  # set min max value to be -2, 2 to avoid overflow.
  return make_reduce_tests(tf.reduce_prod, -2, 2)(options)


@register_make_test_function()
def make_reduce_max_tests(options):
  """Make a set of tests to do max."""
  return make_reduce_tests(tf.reduce_max)(options)


@register_make_test_function()
def make_reduce_min_tests(options):
  """Make a set of tests to do min."""
  return make_reduce_tests(tf.reduce_min)(options)


@register_make_test_function()
def make_reduce_any_tests(options):
  """Make a set of tests to do any."""
  return make_reduce_tests(tf.reduce_any, boolean_tensor_only=True)(options)
