# 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 slice."""
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
from tensorflow.lite.testing.zip_test_utils import TF_TYPE_INFO


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

  # TODO(renjieliu): add test/support for uint8.
  test_parameters = [
      # 4-D
      {
          "dtype": [tf.float32, tf.int32, tf.int64, tf.string],
          "index_type": [tf.int32, tf.int64],
          "input_shape": [[12, 2, 2, 5]],
          "begin": [[0, 0, 0, 0], [1, 0, 1, 0]],
          "size": [[8, 2, 2, 3], [11, 2, 1, 5]],
          "constant_indices": [False],
          "fully_quantize": [False],
      },
      # 2-D
      {
          "dtype": [tf.float32, tf.int32, tf.int64, tf.string],
          "index_type": [tf.int32, tf.int64],
          "input_shape": [[2, 3]],
          "begin": [[0, 0], [1, 0]],
          "size": [[2, 3], [2, 2]],
          "constant_indices": [False],
          "fully_quantize": [False],
      },
      # 4-D with size -1
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[4, 4, 4, 4]],
          "begin": [[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],
                    [0, 0, 0, 1]],
          "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]],
          "constant_indices": [False, True],
          "fully_quantize": [False],
      },
      # last dimension out of index
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[4, 4, 4]],
          "begin": [[3, 3, 4]],
          "size": [[-1, -1, -1]],
          "constant_indices": [False, True],
          "fully_quantize": [False],
      },
      # 4-D
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[12, 2, 2, 5]],
          "begin": [[0, 0, 0, 0], [1, 0, 1, 0]],
          "size": [[8, 2, 2, 3], [11, 2, 1, 5]],
          "constant_indices": [True],
          "fully_quantize": [True],
      },
      # 2-D
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[2, 3]],
          "begin": [[0, 0], [1, 0]],
          "size": [[2, 3], [2, 2]],
          "constant_indices": [True],
          "fully_quantize": [True],
      },
      # 4-D with size -1
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[4, 4, 4, 4]],
          "begin": [[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],
                    [0, 0, 0, 1]],
          "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]],
          "constant_indices": [True],
          "fully_quantize": [True],
      },
      {
          "dtype": [tf.float32],
          "index_type": [tf.int32],
          "input_shape": [[1, 4, 4, 4]],
          "begin": [[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
          "size": [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1], [1, 1, 1, -1]],
          "constant_indices": [True],
          "fully_quantize": [True],
      },
  ]

  def build_graph(parameters):
    """Build graph for slice test."""
    input_tensor = tf.compat.v1.placeholder(
        dtype=parameters["dtype"],
        name="input",
        shape=parameters["input_shape"])
    if parameters["constant_indices"]:
      index_type = TF_TYPE_INFO[parameters["index_type"]][0]
      begin_values = np.array(parameters["begin"]).astype(index_type)
      size_values = np.array(parameters["size"]).astype(index_type)
      out = tf.slice(input_tensor, begin_values, size_values)
      return [input_tensor], [out]
    else:
      begin = tf.compat.v1.placeholder(
          dtype=parameters["index_type"],
          name="begin",
          shape=[len(parameters["input_shape"])])
      size = tf.compat.v1.placeholder(
          dtype=parameters["index_type"],
          name="size",
          shape=[len(parameters["input_shape"])])
      tensors = [input_tensor, begin, size]
      out = tf.slice(input_tensor, begin, size)
      return tensors, [out]

  def build_inputs(parameters, sess, inputs, outputs):
    """Build inputs for slice test."""
    input_values = create_tensor_data(
        parameters["dtype"],
        parameters["input_shape"],
        min_value=-1,
        max_value=1)
    if parameters["constant_indices"]:
      return [input_values], sess.run(
          outputs, feed_dict=dict(zip(inputs, [input_values])))
    else:
      index_type = TF_TYPE_INFO[parameters["index_type"]][0]
      begin_values = np.array(parameters["begin"]).astype(index_type)
      size_values = np.array(parameters["size"]).astype(index_type)
      values = [input_values, begin_values, size_values]
      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=27)
