# Copyright 2017 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.
# ==============================================================================
"""Tests for the pybind11 wrapper of Grappler items."""

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import meta_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.grappler import item
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_v1
from tensorflow.python.platform import test


class ItemTest(test.TestCase):
  """Unit tests for Grappler Item pybind11 wrapper functionality."""

  def _create_sample_metagraph(self, include_train_op=True):
    with ops.Graph().as_default() as g:
      a = constant_op.constant(10)
      b = constant_op.constant(20)
      c = a + b
      z = control_flow_ops.no_op()
      if include_train_op:
        train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
        train_op.append(c)
      return meta_graph.create_meta_graph_def(graph=g), z

  def test_invalid_item_missing_train_op_raises(self):
    """Verifies that Item raises InvalidArgumentError when train_op is missing."""
    mg, _ = self._create_sample_metagraph(include_train_op=False)
    with self.assertRaisesRegex(
        errors_impl.InvalidArgumentError,
        'train_op not specified in the metagraph'):
      item.Item(mg)

  def test_important_ops_identification(self):
    """Verifies important ops are correctly identified from the metagraph."""
    mg, _ = self._create_sample_metagraph(include_train_op=True)
    grappler_item = item.Item(mg)
    op_list = grappler_item.IdentifyImportantOps()
    self.assertCountEqual(['Const', 'Const_1', 'add'], op_list)

  def test_op_properties_extraction(self):
    """Verifies op properties are correctly extracted for graph nodes."""
    mg, z = self._create_sample_metagraph(include_train_op=True)
    grappler_item = item.Item(mg)
    op_properties = grappler_item.GetOpProperties()

    z_prop = op_properties[z.name]
    self.assertEmpty(z_prop)

    const_prop = op_properties['Const']
    self.assertLen(const_prop, 1)
    self.assertEqual(dtypes.int32, const_prop[0].dtype)
    self.assertEqual(tensor_shape.TensorShape([]), const_prop[0].shape)

  def test_tf_item_initial_properties_equal(self):
    """Verifies initial tf_item properties are consistent and equal."""
    mg, _ = self._create_sample_metagraph(include_train_op=True)
    grappler_item = item.Item(mg)
    initial_tf_item = grappler_item.tf_item
    no_change_tf_item = grappler_item.tf_item
    self.assertEqual(initial_tf_item, no_change_tf_item)

  def test_tf_item_device_modification_updates_wrapper(self):
    """Verifies modifying node placement creates a new underlying tf_item."""
    mg, _ = self._create_sample_metagraph(include_train_op=True)
    grappler_item = item.Item(mg)
    initial_tf_item = grappler_item.tf_item
    for node in grappler_item.metagraph.graph_def.node:
      node.device = '/cpu:0'
    new_tf_item = grappler_item.tf_item
    self.assertNotEqual(initial_tf_item, new_tf_item)

  def test_tf_item_identical_device_reassignment_unchanged(self):
    """Verifies re-assigning identical placement keeps tf_item unchanged."""
    mg, _ = self._create_sample_metagraph(include_train_op=True)
    grappler_item = item.Item(mg)
    for node in grappler_item.metagraph.graph_def.node:
      node.device = '/cpu:0'
    new_tf_item = grappler_item.tf_item
    for node in grappler_item.metagraph.graph_def.node:
      node.device = '/cpu:0'
    newest_tf_item = grappler_item.tf_item
    self.assertEqual(new_tf_item, newest_tf_item)

  @test_util.run_v1_only('b/120545219')
  def test_colocation_constraints(self):
    """Verifies colocation constraints are correctly grouped."""
    with ops.Graph().as_default() as g:
      c = constant_op.constant([10])
      v = variable_v1.VariableV1([3], dtype=dtypes.int32)
      i = gen_array_ops.ref_identity(v)
      a = state_ops.assign(i, c)
      train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
      train_op.append(a)
      mg = meta_graph.create_meta_graph_def(graph=g)
      grappler_item = item.Item(mg)
      groups = grappler_item.GetColocationGroups()
      self.assertLen(groups, 1)
      self.assertCountEqual(
          groups[0], ['Assign', 'RefIdentity', 'Variable', 'Variable/Assign'])

  @test_util.run_v1_only('b/120545219')
  def test_colocation_constraints_missing_input(self):
    """Verifies standalone nodes with missing inputs are correctly grouped."""
    with ops.Graph().as_default() as g:
      c = constant_op.constant([10])
      v = variable_v1.VariableV1([3], dtype=dtypes.int32)
      i = gen_array_ops.ref_identity(v)
      a = state_ops.assign(i, c)
      train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
      train_op.append(a)
      mg = meta_graph.create_meta_graph_def(graph=g)
      for node in mg.graph_def.node:
        if node.op == 'Assign':
          del node.input[:]
      grappler_item = item.Item(mg)
      groups = grappler_item.GetColocationGroups()
      self.assertLen(groups, 1)
      self.assertCountEqual(
          groups[0], ['RefIdentity', 'Variable'])


if __name__ == '__main__':
  test.main()