# 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 reshape."""
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_reshape_tests(options):
  """Make a set of tests to do reshape."""

  # All shapes below are suitable for tensors with 420 elements.
  test_parameters = [{
      "dtype": [tf.float32, tf.int32],
      "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
      "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
      "constant_shape": [True, False],
      "fully_quantize": [False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[1]],
      "output_shape": [[]],
      "constant_shape": [True, False],
      "fully_quantize": [False],
  }, {
      "dtype": [tf.float32],
      "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
      "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
      "constant_shape": [True],
      "fully_quantize": [True],
  }]

  def build_graph(parameters):
    """Build the graph for reshape tests."""
    input_tensor = tf.compat.v1.placeholder(
        dtype=parameters["dtype"],
        name="input",
        shape=parameters["input_shape"])

    # Get shape as either a placeholder or constants.
    if parameters["constant_shape"]:
      output_shape = parameters["output_shape"]
      input_tensors = [input_tensor]
    else:
      # The shape of the shape tensor.
      shape_tensor_shape = [len(parameters["output_shape"])]
      output_shape = tf.compat.v1.placeholder(
          dtype=tf.int32, name="output_shape", shape=shape_tensor_shape)
      input_tensors = [input_tensor, output_shape]
    out = tf.reshape(input_tensor, shape=output_shape)
    return input_tensors, [out]

  def build_inputs(parameters, sess, inputs, outputs):
    """Build inputs for reshape op."""

    values = [
        create_tensor_data(
            parameters["dtype"],
            parameters["input_shape"],
            min_value=-1,
            max_value=1)
    ]
    if not parameters["constant_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)