# coding=utf-8
#
# Licensed to the Apache Software Foundation (ASF) under one
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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

m4_changequote(`<!', `!>')

import sys
import numpy as np
import os
import re
from os import path
# Add convex module to the pythonpath.
sys.path.append(path.dirname(path.dirname(path.dirname(path.dirname(path.abspath(__file__))))))
sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))

import unittest
from mock import *
import plpy_mock as plpy

try:
    from pgsanity.pgsanity import check_string as pglint
    pglint("SELECT 1;")
except:
    # Just skip this test if "pip install pgsanity" hasn't been run
    #  or isn't functioning (eg. ecpg isn't in path)
    def pglint(q, s=False):
        return (True, '')

# helper for multiplying array by int
def mult(k,arr):
    return [ k*a for a in arr ]

class MadlibKerasFitEvalTransitionTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras
        self.subject = madlib_keras

        self.model = Sequential()
        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
                         input_shape=(1,1,1,), padding='same'))
        self.model.add(Flatten())

        self.compile_params = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']"
        self.fit_params = "batch_size=1, epochs=1"
        self.model_weights = [3,4,5,6]
        self.serialized_weights = np.array(self.model_weights, dtype=np.float32
                                           ).tostring()
        self.loss = 0.5947071313858032
        self.accuracy = 1.0
        self.dist_key_mapping = [0,1,2]
        self.accessible_gpus_for_seg = [0]

        self.independent_var_real = [[[[0.5]]]] * 10
        self.dependent_var_int = [[0,1]] * 10

        # Params as bytea
        self.independent_var = np.array(self.independent_var_real, dtype=np.float32).tobytes()
        self.dependent_var = np.array(self.dependent_var_int, dtype=np.int16).tobytes()

        self.independent_var_shape = [10,1,1,1]
        self.dependent_var_shape = [10,2]
        # We test on segment 0, which has 3 buffers filled with 10 identical
        #  images each, or 30 images total
        self.total_images_per_seg = [3*len(self.dependent_var_int),20,40]

        self.dummy_prev_weights = 'dummy weights'

        # Mock calls to tf.keras fit
        model_class = self.subject.tf.keras.Model
        model_class.fit = MagicMock()

    def tearDown(self):
        self.module_patcher.stop()
        self.subject.K.clear_session()

    def _test_fit_transition_first_buffer_pass(self, **kwargs):
        ending_image_count = len(self.dependent_var_int)

        previous_state = np.array(self.model_weights, dtype=np.float32)

        new_state = self.subject.fit_transition(
            None, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_state.tostring(), "todo-remove", **kwargs)
        image_count = new_state
        self.assertEqual(ending_image_count, image_count)

    def _test_fit_transition_multiple_model_no_cache_first_buffer_pass(self,
                                                                      **kwargs):
        ending_image_count = len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)

        new_state = self.subject.fit_transition(
            None, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(),
            "todo-remove", True, **kwargs)

        image_count = new_state
        self.assertEqual(ending_image_count, image_count)

    def test_fit_transition_multiple_model_cache_first_buffer_pass(self):
        ending_image_count = len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)

        k = {'GD': {}}
        new_state = self.subject.fit_multiple_transition_caching(
            None, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(), True, **k)

        image_count = new_state
        self.assertEqual(ending_image_count, image_count)
        self.assertTrue('sess' not in k['GD'])
        self.assertTrue('segment_model' not in k['GD'])
        self.assertTrue('cache_set' not in k['GD'])
        self.assertTrue(k['GD']['x_train'])
        self.assertTrue(k['GD']['y_train'])

    def _test_fit_transition_middle_buffer_pass(self, **kwargs):

        starting_image_count = len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        state = starting_image_count
        new_state = self.subject.fit_transition(
            state, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), None, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, **kwargs)

        image_count = new_state
        self.assertEqual(ending_image_count, image_count)

    def _test_fit_transition_multiple_model_no_cache_middle_buffer_pass(self,
                                                                        **kwargs):
        starting_image_count = len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        state = starting_image_count
        new_state = self.subject.fit_transition(
            state, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), None, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, True,
            **kwargs)

        image_count = new_state
        self.assertEqual(ending_image_count, image_count)

    def test_fit_transition_multiple_model_cache_middle_buffer_pass(self):
        starting_image_count = len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)
        x_train = list()
        y_train = list()
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))

        k = {'GD': {'x_train': x_train, 'y_train': y_train}}

        state = starting_image_count
        new_state = self.subject.fit_multiple_transition_caching(
            state, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(), True, **k)

        image_count = new_state
        self.assertEqual(ending_image_count, image_count)
        self.assertTrue('sess' not in k['GD'])
        self.assertTrue('segment_model' not in k['GD'])
        self.assertTrue('cache_set' not in k['GD'])
        self.assertTrue(k['GD']['x_train'])
        self.assertTrue(k['GD']['y_train'])

    def _test_fit_transition_last_buffer_pass(self, **kwargs):

        starting_image_count = 2*len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        state = starting_image_count
        previous_state = np.array(self.model_weights, dtype=np.float32)
        new_state = self.subject.fit_transition(
            state, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), None, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_state.tostring(), "todo-remove",
            **kwargs)
        state = np.fromstring(new_state, dtype=np.float32)
        image_count = state[0]
        # We need to assert that the weights should be multiplied by final image count.
        weights = state[1:]
        multiplied_weights = mult(self.total_images_per_seg[0], self.model_weights)
        self.assertTrue((weights == multiplied_weights).all())
        self.assertEqual(ending_image_count, image_count)

    def _test_internal_keras_eval_transition_first_buffer(self,
                                                          last_iteration = False,
                                                          **kwargs):
        ending_image_count = len(self.dependent_var_int)

        state = [0,0,0]
        new_state = self.subject.internal_keras_eval_transition(
            state, self.dependent_var , self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(),
            self.serialized_weights, self.compile_params, 0,
            self.dist_key_mapping, 0, 4,
            self.total_images_per_seg, False, self.accessible_gpus_for_seg,
            last_iteration, None, **kwargs)

        agg_loss, agg_accuracy, image_count = new_state

        self.assertEqual(ending_image_count, image_count)
        # loss and accuracy should be unchanged
        self.assertAlmostEqual(self.loss * image_count, agg_loss, 4)
        self.assertAlmostEqual(self.accuracy * image_count, agg_accuracy, 4)

    def _test_internal_keras_eval_transition_last_buffer(self,
                                                     last_iteration = False,
                                                     **kwargs):
        starting_image_count = 2*len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        state = [self.loss * starting_image_count,
                 self.accuracy * starting_image_count,
                 starting_image_count]

        new_state = self.subject.internal_keras_eval_transition(
            state, self.dependent_var , self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(),
            'dummy_model_weights', None, 0,
            self.dist_key_mapping, 0, 4,
            self.total_images_per_seg, False, self.accessible_gpus_for_seg,
            last_iteration, **kwargs)
        agg_loss, agg_accuracy, image_count = new_state

        self.assertEqual(ending_image_count, image_count)
        # loss and accuracy should be unchanged
        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)

    def _test_internal_keras_eval_transition_middle_buffer(self,
                                                           last_iteration = False,
                                                           **kwargs):
        starting_image_count = len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)


        state = [self.loss * starting_image_count,
                 self.accuracy * starting_image_count, starting_image_count]

        new_state = self.subject.internal_keras_eval_transition(
            state, self.dependent_var , self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(),
            'dummy_model_weights', None, 0,
            self.dist_key_mapping, 0, 4,
            self.total_images_per_seg, False, self.accessible_gpus_for_seg,
            last_iteration, **kwargs)

        agg_loss, agg_accuracy, image_count = new_state

        self.assertEqual(ending_image_count, image_count)
        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)

    def _test_fit_transition_multiple_model_no_cache_last_buffer_pass(self,
                                                                     **kwargs):
        starting_image_count = 2*len(self.dependent_var_int)

        state = starting_image_count
        new_state = self.subject.fit_transition(
            state , self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), None, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, self.dummy_prev_weights, "todo-remove",
            True, **kwargs)

        state = np.fromstring(new_state, dtype=np.float32)
        weights = np.rint(state[0:]).astype(np.int)

        ## image count should not be added to the final state of
        # fit multiple
        self.assertEqual(len(self.model_weights), len(weights))

    def test_fit_transition_multiple_model_cache_last_buffer_pass(self):
        starting_image_count = 2*len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)
        x_train = list()
        y_train = list()
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))

        k = {'GD': {'x_train': x_train, 'y_train': y_train}}

        state = starting_image_count
        graph1 = self.subject.tf.get_default_graph()
        new_state = self.subject.fit_multiple_transition_caching(
            state, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(), False, **k)
        graph2 = self.subject.tf.get_default_graph()
        self.assertNotEquals(graph1, graph2)
        state = np.fromstring(new_state, dtype=np.float32)
        weights = np.rint(state[0:]).astype(np.int)

        ## image count should not be added to the final state of
        # fit multiple
        self.assertEqual(len(self.model_weights), len(weights))

        self.assertTrue('sess' not in k['GD'])
        self.assertTrue('segment_model' not in k['GD'])
        self.assertTrue(k['GD']['cache_set'])
        self.assertTrue(k['GD']['x_train'])
        self.assertTrue(k['GD']['y_train'])

    def test_fit_transition_multiple_model_cache_filled_pass(self):
        starting_image_count = 2*len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)
        x_train = list()
        y_train = list()
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))

        self.subject.compile_and_set_weights(self.model, self.compile_params,
                                                     '/cpu:0', self.serialized_weights)
        s1 = self.subject.K.get_session()
        k = {'GD': {'x_train': x_train, 'y_train': y_train, 'cache_set': True,
                    'sess': s1, 'segment_model': self.model}}
        graph1 = self.subject.tf.get_default_graph()
        new_state = self.subject.fit_multiple_transition_caching(
            None, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(), False, **k)
        graph2 = self.subject.tf.get_default_graph()
        self.assertNotEquals(graph1, graph2)
        state = np.fromstring(new_state, dtype=np.float32)
        weights = np.rint(state[0:]).astype(np.int)

        ## image count should not be added to the final state of
        # fit multiple
        self.assertEqual(len(self.model_weights), len(weights))

        self.assertTrue('sess' not in k['GD'])
        self.assertTrue('segment_model' not in k['GD'])
        self.assertTrue(k['GD']['cache_set'])
        self.assertTrue(k['GD']['x_train'])
        self.assertTrue(k['GD']['y_train'])

    def test_fit_transition_multiple_model_cache_filled_final_training_pass(self):
        starting_image_count = 2*len(self.dependent_var_int)
        ending_image_count = starting_image_count + len(self.dependent_var_int)

        previous_weights = np.array(self.model_weights, dtype=np.float32)
        x_train = list()
        y_train = list()
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))
        x_train.append(self.subject.np_array_float32(self.independent_var, self.independent_var_shape))
        y_train.append(self.subject.np_array_int16(self.dependent_var, self.dependent_var_shape))

        k = {'GD': {'x_train': x_train, 'y_train': y_train, 'cache_set': True}}
        graph1 = self.subject.tf.get_default_graph()
        new_state = self.subject.fit_multiple_transition_caching(
            None, self.dependent_var, self.independent_var,
            self.dependent_var_shape, self.independent_var_shape,
            self.model.to_json(), self.compile_params, self.fit_params, 0,
            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
            self.accessible_gpus_for_seg, previous_weights.tostring(), True, **k)
        graph2 = self.subject.tf.get_default_graph()
        self.assertNotEquals(graph1, graph2)

        state = np.fromstring(new_state, dtype=np.float32)
        weights = np.rint(state[0:]).astype(np.int)

        ## image count should not be added to the final state of
        # fit multiple
        self.assertEqual(len(self.model_weights), len(weights))

        self.assertTrue('sess' not in k['GD'])
        self.assertTrue('segment_model' not in k['GD'])
        self.assertTrue('cache_set' not in k['GD'])
        self.assertTrue('x_train' not in k['GD'])
        self.assertTrue('y_train' not in k['GD'])

    ############### GRAPH AND SESSION TESTS ################################
    def test_fit_eval_2_iterations_mcf_null_gpdb(self):
        kwargs = {'GD': {}}
        GD = kwargs['GD']

        ######################### fit for 2 iterations ##########
        # iteration 1
        first_iter_keras_sess = self._run_fit_iteration(**kwargs)
        self._assert_keras_session_same_as_gd_session(GD)

        first_iter_tf_graph = self.subject.tf.get_default_graph()

        # iteration 2 (last iteration)
        last_iter_keras_sess = self._run_fit_iteration(**kwargs)
        self._assert_keras_session_same_as_gd_session(GD)

        last_iter_tf_graph = self.subject.tf.get_default_graph()

        self.assertEquals(first_iter_keras_sess, last_iter_keras_sess)
        self.assertEquals(first_iter_tf_graph, last_iter_tf_graph)

        ###################### eval transition for last iteration ###########
        self._run_eval_iteration(True, last_iter_keras_sess, last_iter_tf_graph, **kwargs)
        eval_last_iter_keras_sess = self.subject.K.get_session()
        eval_last_iter_tf_graph = self.subject.tf.get_default_graph()

        self.assertNotEquals(eval_last_iter_keras_sess, last_iter_keras_sess)
        self.assertNotEquals(eval_last_iter_tf_graph, last_iter_tf_graph)
        self._assert_gd_cleared(GD)

    def test_fit_eval_2_iterations_mcf_1_gpdb(self):
        kwargs = {'GD': {}}
        GD = kwargs['GD']

        ######################### fit + eval for 2 iterations ##########
        # iteration 1 fit
        first_iter_keras_sess = self._run_fit_iteration(**kwargs)
        self._assert_keras_session_same_as_gd_session(GD)

        first_iter_tf_graph = self.subject.tf.get_default_graph()

        # iteration 1 eval
        self._run_eval_iteration(False, first_iter_keras_sess, first_iter_tf_graph, **kwargs)
        self._assert_keras_session_same_as_gd_session(GD)

        eval_first_iter_keras_sess = self.subject.K.get_session()
        eval_first_iter_tf_graph = self.subject.tf.get_default_graph()

        self.assertEquals(eval_first_iter_keras_sess, first_iter_keras_sess)
        self.assertEquals(eval_first_iter_tf_graph, first_iter_tf_graph)

        # iteration 2 fit (last iteration)
        last_iter_keras_sess = self._run_fit_iteration(**kwargs)
        self._assert_keras_session_same_as_gd_session(GD)

        last_iter_tf_graph = self.subject.tf.get_default_graph()

        self.assertEquals(first_iter_keras_sess, last_iter_keras_sess)
        self.assertEquals(first_iter_tf_graph, last_iter_tf_graph)

        # iteration 2 eval (last iteration)
        # eval and fit use the same session & graph for all iterations.
        # After the last call to eval(last iteration), we want to assert
        # the session was closed and the graph cleared out.
        # To assert this we call get_session() and get_default_graph()
        # that will give a new session and a new graph and assert its not
        # equal to the prev iteration session and graph.
        self._run_eval_iteration(True, last_iter_keras_sess, last_iter_tf_graph, **kwargs)

        eval_last_iter_keras_sess = self.subject.K.get_session()
        eval_last_iter_tf_graph = self.subject.tf.get_default_graph()

        self.assertNotEquals(eval_last_iter_keras_sess, last_iter_keras_sess)
        self.assertNotEquals(eval_last_iter_tf_graph, last_iter_tf_graph)
        self._assert_gd_cleared(GD)

    def test_fit_multiple_2_iterations(self):
        kwargs = {'GD': {}}
        GD = kwargs['GD']

        ############ fit multiple for 2 iterations ##########
        # iteration 1
        # first_iter_tf_graph is used to assert that calling fit_multiple closes the tf session
        # and graph at the last buffer.
        # It is fetched prior to calling the fit_transition(from fit_multiple) as when we create
        # a session inside fit_transition, instead of creating a new graph it will use first_iter_tf_graph.
        # This enables us to do the not equals assert.
        first_iter_tf_graph = self.subject.tf.get_default_graph()
        first_iter_keras_sess = self._run_fit_multiple_iteration(**kwargs)
        self._assert_gd_cleared(GD)

        # iteration 2 (last iteration)
        last_iter_tf_graph = self.subject.tf.get_default_graph()
        last_iter_keras_sess = self._run_fit_multiple_iteration(**kwargs)
        self._assert_gd_cleared(GD)

        self.assertNotEquals(first_iter_keras_sess, last_iter_keras_sess)
        self.assertNotEquals(first_iter_tf_graph, last_iter_tf_graph)

    def test_eval_multiple_any_iteration(self):
        # This test tests 2 things:
        # 1. Calling eval_transition from fit_multiple
        # 2. Calling eval_transition from evaluate directly
        kwargs = {'GD': {}}
        GD = kwargs['GD']

        # eval_iter_tf_graph1 is used to assert that calling eval clears the tf session and graph
        # It is fetched prior to calling the eval_transition as when we create a session inside
        # eval_transition, instead of creating a new graph it will use eval_iter_tf_graph1.
        # This enables us to do the not equals assert.
        eval_iter_tf_graph1 = self.subject.tf.get_default_graph()
        eval_iter_keras_sess1 = self._run_eval_iteration(True, None, None, True, **kwargs)
        eval_iter_keras_sess2 = self.subject.K.get_session()
        eval_iter_tf_graph2 = self.subject.tf.get_default_graph()

        self.assertNotEquals(eval_iter_keras_sess1, eval_iter_keras_sess2)
        self.assertNotEquals(eval_iter_tf_graph1, eval_iter_tf_graph2)
        self._assert_gd_cleared(GD)

    def _run_eval_iteration(self, final_iteration, prev_keras_sess, prev_tf_graph, called_from_fit_multiple=False, **kwargs):
        self._test_internal_keras_eval_transition_first_buffer(final_iteration,
                                                               **kwargs)
        self._assert_gd_is_valid(kwargs['GD'])
        self._assert_keras_session_same_as_gd_session(kwargs['GD'])

        eval_first_buffer_keras_sess = kwargs['GD']['sess']
        self.assertFalse(eval_first_buffer_keras_sess._closed)
        eval_first_buffer_tf_graph = self.subject.tf.get_default_graph()

        if not called_from_fit_multiple:
            self.assertEquals(eval_first_buffer_keras_sess, prev_keras_sess)
            self.assertEquals(eval_first_buffer_tf_graph, prev_tf_graph)

        self._test_internal_keras_eval_transition_middle_buffer(final_iteration,
                                                                **kwargs )
        self._assert_gd_is_valid(kwargs['GD'])
        self._assert_keras_session_same_as_gd_session(kwargs['GD'])
        self.assertFalse(eval_first_buffer_keras_sess._closed)

        self._test_internal_keras_eval_transition_last_buffer(final_iteration,
                                                              **kwargs)
        if final_iteration:
            self._assert_gd_cleared(kwargs['GD'])
            self.assertTrue(eval_first_buffer_keras_sess._closed)
        else:
            self._assert_gd_is_valid(kwargs['GD'])
            self.assertFalse(eval_first_buffer_keras_sess._closed)
        return eval_first_buffer_keras_sess

    def _run_fit_iteration(self, **kwargs):
        self._test_fit_transition_first_buffer_pass(**kwargs)
        gd_first_buffer = kwargs['GD']
        self._assert_gd_is_valid(gd_first_buffer)
        iter_sess = gd_first_buffer['sess']
        self.assertFalse(iter_sess._closed)
        self._assert_keras_session_same_as_gd_session(gd_first_buffer)

        self._test_fit_transition_middle_buffer_pass(**kwargs)
        gd_middle_buffer = kwargs['GD']
        self._assert_gd_is_valid(gd_middle_buffer)
        self.assertFalse(iter_sess._closed)

        self._test_fit_transition_last_buffer_pass(**kwargs)
        gd_last_buffer = kwargs['GD']
        self._assert_gd_is_valid(gd_last_buffer)
        self.assertFalse(iter_sess._closed)
        return iter_sess

    def _run_fit_multiple_iteration(self, **kwargs):
        self._test_fit_transition_multiple_model_no_cache_first_buffer_pass(**kwargs)
        self._assert_gd_is_valid(kwargs['GD'])
        self._assert_keras_session_same_as_gd_session(kwargs['GD'])
        iter_sess = kwargs['GD']['sess']
        self.assertFalse(iter_sess._closed)

        self._test_fit_transition_multiple_model_no_cache_middle_buffer_pass(**kwargs)
        self._assert_gd_is_valid(kwargs['GD'])
        self._assert_keras_session_same_as_gd_session(kwargs['GD'])
        self.assertFalse(iter_sess._closed)

        self._test_fit_transition_multiple_model_no_cache_last_buffer_pass(**kwargs)
        self._assert_gd_cleared(kwargs['GD'])
        self.assertTrue(iter_sess._closed)
        return iter_sess

    def _assert_keras_session_same_as_gd_session(self, gd):
        sess = self.subject.K.get_session()
        self.assertEquals(sess, gd['sess'])

    def _assert_gd_cleared(self, gd):
        self.assertEquals(0, len(gd.keys()))

    def _assert_gd_is_valid(self, gd):
        self.assertTrue(gd['sess'])
        self.assertTrue(gd['segment_model'])

    ################################################################

    def test_fit_transition_first_tuple_none_ind_var_dep_var(self):
        k = {}
        self.assertEqual('dummy_state',
                         self.subject.fit_transition('dummy_state', [0], None,
                                                     'noshape', 'noshape',
                                                     'dummy_model_json', "foo", "bar",
                                                     1, [0,1,2], 0, 4, [3,3,3], False,
                                                     [0], 'dummy_prev_state', **k))
        self.assertEqual('dummy_state',
                         self.subject.fit_transition('dummy_state', None, [[0.5]],
                                                     'noshape', 'noshape',
                                                     'dummy_model_json', "foo", "bar",
                                                     1, [0,1,2], 0, 4, [3,3,3], False,
                                                     [0], 'dummy_prev_state', **k))
        self.assertEqual('dummy_state',
                         self.subject.fit_transition('dummy_state', None, None,
                                                     'noshape', 'noshape',
                                                     'dummy_model_json', "foo", "bar",
                                                     1, [0,1,2], 0, 4, [3,3,3], False,
                                                     [0], 'dummy_prev_state', **k))

    def test_fit_merge(self):
        image_count = self.total_images_per_seg[0]
        state1 = [image_count]
        state1.extend(mult(3,self.model_weights))
        state1 = np.array(state1, dtype=np.float32)
        state2 = [image_count+30]
        state2.extend(mult(2,self.model_weights))
        state2 = np.array(state2, dtype=np.float32)
        merged_state = self.subject.fit_merge(state1.tostring(),state2.tostring())
        state = np.fromstring(merged_state, dtype=np.float32)
        image_count_total = state[0]
        weights = np.rint(state[1:]).astype(np.int)

        self.assertEqual( 2*image_count+30 , image_count_total )
        self.assertTrue( (mult(5,self.model_weights) == weights).all())

    def test_fit_merge_none_first(self):
        image_count = self.total_images_per_seg[0]
        input_state = [image_count]
        input_state.extend(self.model_weights)
        input_state = np.array(input_state, dtype=np.float32)
        merged_state = self.subject.fit_merge(None, input_state.tostring())
        state = np.fromstring(merged_state, dtype=np.float32)
        image_count_total = state[0]
        weights = np.rint(state[1:]).astype(np.int)

        self.assertEqual(image_count, image_count_total)
        self.assertTrue((self.model_weights == weights).all())

    def test_fit_merge_none_second(self):
        image_count = self.total_images_per_seg[0]
        input_state = [image_count]
        input_state.extend(self.model_weights)
        input_state = np.array(input_state, dtype=np.float32)
        merged_state = self.subject.fit_merge(input_state.tostring(), None)
        state = np.fromstring(merged_state, dtype=np.float32)
        image_count_total = state[0]
        weights = np.rint(state[1:]).astype(np.int)

        self.assertEqual(image_count, image_count_total)
        self.assertTrue((self.model_weights == weights).all())

    def test_fit_merge_both_none(self):
        result = self.subject.fit_merge(None,None)
        self.assertEqual(None, result)

    def test_fit_final(self):
        image_count = self.total_images_per_seg[0]
        input_state = [image_count]
        input_state.extend(mult(image_count,self.model_weights))
        input_state = np.array(input_state, dtype=np.float32)

        output_state = self.subject.fit_final(input_state.tostring())
        output_state = np.fromstring(output_state, dtype=np.float32)
        weights = np.rint(output_state).astype(np.int)

        self.assertTrue((self.model_weights == weights).all())

    def test_fit_final_none(self):
        result = self.subject.fit_final(None)
        self.assertEqual(result, None)

    def test_fit_final_image_count_zero(self):
        input_state = [0]
        input_state.extend(self.model_weights)
        input_state = np.array(input_state, dtype=np.float32)

        with self.assertRaises(plpy.PLPYException):
            result = self.subject.fit_final(input_state.tostring())

    def test_should_compute_metrics_this_iter_last_iter_case1(self):
        res = self.subject.should_compute_metrics_this_iter(5, 5, 5)
        self.assertEqual(True, res)
    def test_should_compute_metrics_this_iter_last_iter_case2(self):
        res = self.subject.should_compute_metrics_this_iter(5, 3, 5)
        self.assertEqual(True, res)
    def test_should_compute_metrics_this_iter_before_interval(self):
        res = self.subject.should_compute_metrics_this_iter(1, 3, 5)
        self.assertEqual(False, res)
    def test_should_compute_metrics_this_iter_after_interval(self):
        res = self.subject.should_compute_metrics_this_iter(4, 3, 5)
        self.assertEqual(False, res)
    def test_should_compute_metrics_this_iter_at_interval(self):
        res = self.subject.should_compute_metrics_this_iter(3, 3, 5)
        self.assertEqual(True, res)
    def test_should_compute_metrics_this_iter_every_iter(self):
        res = self.subject.should_compute_metrics_this_iter(2, 1, 5)
        self.assertEqual(True, res)


class InternalKerasPredictTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras_predict
        self.subject = madlib_keras_predict

        self.model = Sequential()
        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
                              input_shape=(1,1,1,), padding='same'))
        self.model.add(Flatten())

        self.all_seg_ids = [0,1,2]

        self.independent_var = [[[240]]]
        self.total_images_per_seg = [3,3,4]
        self.subject.K.set_session = Mock()
        self.subject.clear_keras_session = Mock()


    def tearDown(self):
        self.module_patcher.stop()

    def test_predict_first_image_pass_gpdb(self):
        self.subject.is_platform_pg = Mock(return_value = False)
        model_weights = [1, 2, 3, 4]
        serialized_weights = np.array(model_weights, dtype=np.float32).tostring()

        k = {'SD': {}}
        is_response = True
        result = self.subject.internal_keras_predict(
            self.independent_var, self.model.to_json(),
            serialized_weights, 255, 0, self.all_seg_ids,
            self.total_images_per_seg, False, 0, 4, **k)
        self.assertEqual(2, len(result))
        self.assertEqual(1,  k['SD']['row_count'])
        self.assertEqual(True, 'segment_model_predict' in k['SD'])

    def test_predict_middle_image_pass_gpdb(self):
        self.subject.is_platform_pg = Mock(return_value = False)

        k = {'SD': { 'row_count': 1}}
        k['SD']['segment_model_predict'] = self.model
        is_response = True
        result = self.subject.internal_keras_predict(
            self.independent_var, None, None, 255, 0,
            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
        self.assertEqual(2, len(result))
        self.assertEqual(2,  k['SD']['row_count'])
        self.assertEqual(True, 'segment_model_predict' in k['SD'])

    def test_predict_last_image_pass_gpdb(self):
        self.subject.is_platform_pg = Mock(return_value = False)
        self.model.add(Dense(3))

        k = {'SD': { 'row_count': 2}}
        k['SD']['segment_model_predict'] = self.model
        is_response = True
        result = self.subject.internal_keras_predict(
            self.independent_var, None, None, 255, 0,
            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
        self.assertEqual(3, len(result))
        self.assertEqual(False, 'row_count' in k['SD'])
        self.assertEqual(False, 'segment_model_predict' in k['SD'])

        k = {'SD': { 'row_count': 2}}
        k['SD']['segment_model_predict'] = self.model
        is_response = False
        result = self.subject.internal_keras_predict(
            self.independent_var, None, None, 255, 0,
            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)

        # we except len(result) to be 3 because we have 3 dense layers in the
        # architecture
        self.assertEqual(3, len(result))
        self.assertEqual(False, 'row_count' in k['SD'])
        self.assertEqual(False, 'segment_model_predict' in k['SD'])


    def test_predict_error_should_clear_sd(self):
        self.subject.is_platform_pg = Mock(return_value = False)
        self.model.add(Dense(3))

        # inject error by passing 0 as the normalizing const so that we get a
        # divide by zero error
        normalizing_const = 0
        k = {'SD':{}}
        is_response = True
        with self.assertRaises(plpy.PLPYException):
            self.subject.internal_keras_predict(
                self.independent_var, None, None, normalizing_const,
                0, self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
        self.assertEqual(False, 'row_count' in k['SD'])
        self.assertEqual(False, 'segment_model_predict' in k['SD'])


class MadlibKerasPredictBYOMTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        self.num_classes = 5
        self.model = Sequential()
        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
                              input_shape=(1,1,1,), padding='same'))
        self.model.add(Dense(self.num_classes))

        self.pred_type = 'prob'
        self.use_gpus = False
        self.class_values = ['foo', 'bar', 'baaz', 'foo2', 'bar2']
        self.normalizing_const = 255.0

        import madlib_keras_predict
        self.module = madlib_keras_predict
        self.module.get_model_arch_weights = Mock(return_value=(
            self.model.to_json(), 'weights'))
        self.module.InputValidator.validate_predict_byom_tables = Mock()
        self.module.InputValidator.validate_input_shape = Mock()
        self.module.BasePredict.call_internal_keras = Mock()

    def tearDown(self):
        self.module_patcher.stop()

    def test_predictbyom_defaults_1(self):
        self.module.get_accessible_gpus_for_seg = Mock(return_value = [2,2,2])
        res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
                                 'model_id', 'test_table', 'id_col',
                                 'independent_varname', 'output_table', None,
                                 True, None, None)
        self.assertEqual('prob', res.pred_type)
        self.assertEqual(2, res.gpus_per_host)
        self.assertEqual([0,1,2,3,4], res.class_values)
        self.assertEqual(1.0, res.normalizing_const)

    def test_predictbyom_defaults_2(self):
        res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
                                       'model_id', 'test_table', 'id_col',
                                       'independent_varname', 'output_table',
                                       self.pred_type, self.use_gpus,
                                       self.class_values, self.normalizing_const)
        self.assertEqual('prob', res.pred_type)
        self.assertEqual(0, res.gpus_per_host)
        self.assertEqual(['foo', 'bar', 'baaz', 'foo2', 'bar2'], res.class_values)
        self.assertEqual(255.0, res.normalizing_const)

    def test_predictbyom_exception_invalid_params(self):
        with self.assertRaises(plpy.PLPYException) as error:
            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
                                     'model_id', 'test_table', 'id_col',
                                     'independent_varname', 'output_table',
                                     'invalid_pred_type', self.use_gpus,
                                     self.class_values, self.normalizing_const)
        self.assertIn('invalid_pred_type', str(error.exception))

        with self.assertRaises(plpy.PLPYException) as error:
            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
                                     'model_id', 'test_table', 'id_col',
                                     'independent_varname', 'output_table',
                                     self.pred_type, self.use_gpus,
                                     ["foo", "bar", "baaz"], self.normalizing_const)
        self.assertIn('class values', str(error.exception).lower())

        with self.assertRaises(plpy.PLPYException) as error:
            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
                                     'model_id', 'test_table', 'id_col',
                                     'independent_varname', 'output_table',
                                     self.pred_type, self.use_gpus,
                                     self.class_values, 0)
        self.assertIn('normalizing const', str(error.exception).lower())


class MadlibKerasWrapperTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()

        import madlib_keras_wrapper
        self.subject = madlib_keras_wrapper

        self.use_gpus = False

    def tearDown(self):
        self.module_patcher.stop()

    def test_get_gpu_memory_fraction(self):

        gpus_per_host = 4
        segments_per_host = 4
        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
        self.assertEqual(result, 0.9)

        gpus_per_host = 10
        segments_per_host = 4
        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
        self.assertEqual(result, 0.9)

        gpus_per_host = 2
        segments_per_host = 6
        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
        self.assertEqual(result, 0.3)

        gpus_per_host = 1
        segments_per_host = 4
        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
        self.assertEqual(result, 0.225)

    def test_get_device_name_and_set_cuda_env_postgres(self):
        self.subject.is_platform_pg = Mock(return_value = True)

        seg_id = -1
        gpus_per_host = 3

        self.assertEqual('/gpu:0', self.subject.get_device_name_and_set_cuda_env(
            gpus_per_host, seg_id ))
        self.assertEqual('0,1,2', os.environ['CUDA_VISIBLE_DEVICES'])

        gpus_per_host = 0
        self.assertEqual('/cpu:0', self.subject.get_device_name_and_set_cuda_env(
            gpus_per_host, seg_id ))
        self.assertEqual('-1', os.environ['CUDA_VISIBLE_DEVICES'])

    def test_get_device_name_and_set_cuda_env_gpdb(self):
        self.subject.is_platform_pg = Mock(return_value = False)

        seg_id=3
        gpus_per_host=2
        self.assertEqual('/gpu:0', self.subject.get_device_name_and_set_cuda_env(
            gpus_per_host, seg_id))
        self.assertEqual('1', os.environ['CUDA_VISIBLE_DEVICES'])

        gpus_per_host=0
        self.assertEqual('/cpu:0', self.subject.get_device_name_and_set_cuda_env(
            gpus_per_host, seg_id))
        self.assertEqual('-1', os.environ['CUDA_VISIBLE_DEVICES'])


    def test_split_and_strip(self):
        self.assertEqual(('a','b'), self.subject.split_and_strip(' a = b '))

    def test_parse_optimizer(self):
        opt_name = 'SGD'
        final_args = {'lr':0.01, 'decay':1e-6, 'nesterov':True}
        compile_dict = {}
        compile_dict['optimizer']='SGD(lr=0.01, decay=1e-6, nesterov=True)'
        result_name, result_params = self.subject.parse_optimizer(compile_dict)

        self.assertEqual(result_name, opt_name)
        self.assertDictEqual(result_params, final_args)

    def test_validate_and_literal_eval_keys(self):
        test_dict = {'batch_size':'2', 'epochs':'1', 'verbose':'0'}
        target_dict = {'batch_size':2, 'epochs':1, 'verbose':0}
        literal_eval_fit_params = ['batch_size','epochs','verbose','shuffle',
                           'class_weight','initial_epoch','steps_per_epoch']
        accepted_fit_params = literal_eval_fit_params + ['shuffle']
        result_params = self.subject.validate_and_literal_eval_keys(
                            test_dict,
                            literal_eval_fit_params,
                            accepted_fit_params)
        self.assertDictEqual(result_params, target_dict)

    def test_parse_and_validate_fit_params(self):
        result = {'batch_size':2, 'epochs':1, 'verbose':0}
        self.assertDictEqual(result, self.subject.parse_and_validate_fit_params('batch_size=2, epochs=1, verbose=0'))

    def test_parse_and_validate_fit_params(self):
        test_str = "batch_size=2, epochs=1, verbose=0"
        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0}
        result_params = self.subject.parse_and_validate_fit_params(test_str)
        self.assertDictEqual(result_params, fit_dict)

        test_str = "batch_size=2, epochs=1, verbose=0, shuffle = 'batch'"
        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0, 'shuffle':'batch'}
        result_params = self.subject.parse_and_validate_fit_params(test_str)
        self.assertDictEqual(result_params, fit_dict)

        test_str = "batch_size=2, epochs=1, verbose=0, shuffle = True"
        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0, 'shuffle':True}
        result_params = self.subject.parse_and_validate_fit_params(test_str)
        self.assertDictEqual(result_params, fit_dict)

        test_str = ""
        fit_dict = {}
        result_params = self.subject.parse_and_validate_fit_params(test_str)
        self.assertDictEqual(result_params, fit_dict)

    def test_parse_and_validate_fit_params_duplicate_params(self):
        test_str = "batch_size=1, batch_size=10, verbose=0"
        fit_dict = {'batch_size':10, 'verbose':0}
        result_params = self.subject.parse_and_validate_fit_params(test_str)
        self.assertDictEqual(result_params, fit_dict)

    def test_parse_and_validate_fit_params_invalid_fit_param_fail(self):
        test_str = "does_not_exist=2, epochs=1, verbose=0"
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.parse_and_validate_fit_params(test_str)

        self.assertIn('not currently accepted', str(error.exception))

        test_str = "batch_size=not_lit_eval(1), epochs=1, verbose=0"
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.parse_and_validate_fit_params(test_str)

        self.assertIn('invalid input value', str(error.exception))

    def test_parse_and_validate_compile_params(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
                   "loss='categorical_crossentropy', metrics=['accuracy']"
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['accuracy'], 'loss':'categorical_crossentropy'}
        opt_name,opt_args,result_params = self.subject.parse_and_validate_compile_params(test_str)
        self.assertDictEqual(result_params, compile_dict)

        # Test without the metrics
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
                   "loss='categorical_crossentropy'"
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'loss':'categorical_crossentropy'}
        opt_name,opt_args,result_params = self.subject.parse_and_validate_compile_params(test_str)
        self.assertDictEqual(result_params, compile_dict)

    def test_validate_metrics_None_pass(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['accuracy'], 'loss':'categorical_crossentropy'}
        self.subject._validate_metrics(compile_dict)

    def test_validate_metrics_empty_pass(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':[], 'loss':'categorical_crossentropy'}
        self.subject._validate_metrics(compile_dict)

    def test_validate_metrics_two_params_fail(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['accuracy','mae'], 'loss':'categorical_crossentropy'}
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject._validate_metrics(compile_dict)

    def test_validate_metrics_one_unsupported_fail(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['sparse_categorical_accuracy'], 'loss':'categorical_crossentropy'}
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject._validate_metrics(compile_dict)

    def test_validate_metrics_two_unsupported_fail(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['sparse_categorical_accuracy', 'sparse_categorical_crossentropy'], 'loss':'categorical_crossentropy'}
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject._validate_metrics(compile_dict)

    def test_validate_metrics_one_supported_one_unsupported_fail(self):
        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
                        'metrics':['accuracy', 'sparse_categorical_crossentropy'], 'loss':'categorical_crossentropy'}
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject._validate_metrics(compile_dict)

    def test_parse_and_validate_compile_params_default_optimizer_pass(self):
        test_str = "optimizer='SGD', loss='categorical_crossentropy'"
        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
        self.assertEqual('SGD', result_dict['optimizer'])

        test_str = "optimizer='sgd()', loss='categorical_crossentropy'"
        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
        self.assertEqual('sgd()', result_dict['optimizer'])

    def test_parse_and_validate_compile_params_duplicate_loss(self):
        test_str = "optimizer='SGD', loss='loss1', metrics=['accuracy'], loss='loss2'"
        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
        self.assertEqual('loss2', result_dict['loss'])

    def test_parse_and_validate_compile_params_no_optimizer_fail(self):
        test_str = "loss='categorical_crossentropy', metrics=['accuracy']"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_compile_params_no_loss_fail(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
                   "metrics=['accuracy']"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_compile_params_unsupported_loss_fail(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
                   "metrics=['accuracy'], loss='sparse_categorical_crossentropy'"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_compile_params_dict_metrics_fail(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
                   "loss='categorical_crossentropy', metrics={'0':'accuracy'}"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_compile_params_tensor_loss_weights_fail(self):

        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy', metrics=['accuracy']," \
                   " loss_weights = keras.layers.Input(shape=(32,))"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_compile_params_list_dict_sample_weight_mode_fail(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy', metrics=['accuracy']," \
                   " sample_weight_mode = [0,1]"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy', metrics=['accuracy']," \
                   " sample_weight_mode = {'0':'1'}"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)


    def test_parse_and_validate_compile_params_target_tensors_fail(self):
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy', metrics=['accuracy']," \
                   " target_tensors = keras.layers.Input(shape=(32,))"

        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_compile_params(test_str)

    def test_parse_and_validate_fit_params_callbacks_fail(self):

        test_str = "batch_size=2, epochs=1, verbose=0, callbacks=keras.callbacks.Callback()"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_fit_params_validation_split_fail(self):

        test_str = "batch_size=2, epochs=1, verbose=0, validation_split=0.1"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_fit_params_validation_data_fail(self):

        test_str = "batch_size=2, epochs=1, verbose=0, validation_data=(1,2,3)"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_fit_params_sample_weight_fail(self):

        test_str = "batch_size=2, epochs=1, verbose=0, sample_weight=np.array([1,2,3])"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_fit_params_validation_steps_fail(self):

        test_str = "batch_size=2, epochs=1, verbose=0, validation_steps=1"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_fit_params_validation_freq_fail(self):
        test_str = "batch_size=2, epochs=1, verbose=0, validation_freq=1"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

        test_str = "batch_size=2, epochs=1, verbose=0, validation_freq=[1]"
        with self.assertRaises(plpy.PLPYException):
            self.subject.parse_and_validate_fit_params(test_str)

    def test_parse_and_validate_compile_params_syntax_error_fail(self):
        #missing end parentheses
        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True"
        with self.assertRaises(ValueError) as error:
            self.subject.parse_and_validate_compile_params(test_str)
        self.assertIn('could not convert string to float', str(error.exception))

        #missing beginning parentheses
        test_str = "optimizer=SGDlr=0.01, decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy'"
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.parse_and_validate_compile_params(test_str)
        self.assertIn('not currently accepted', str(error.exception))

        #missing comma
        test_str = "optimizer=SGD(lr=0.01 decay=1e-6, nesterov=True)," \
                   " loss='categorical_crossentropy'"
        with self.assertRaises(ValueError) as error:
            self.subject.parse_and_validate_compile_params(test_str)
        self.assertIn('invalid literal for float', str(error.exception))

    def test_parse_and_validate_fit_params_invalid_optimizer_fail(self):
        test_str = "optimizer='SGD1', loss='categorical_crossentropy'"
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.parse_and_validate_compile_params(test_str)
        self.assertIn('invalid optimizer', str(error.exception))


class MadlibKerasFitCommonValidatorTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras_validator
        self.subject = madlib_keras_validator

    def tearDown(self):
        self.module_patcher.stop()


    def test_is_valid_metrics_compute_frequency_True_None(self):
        self.subject.FitCommonValidator._validate_common_args = Mock()
        obj = self.subject.FitCommonValidator(
            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
            'dep_varname', 'independent_varname', 5, None, False, False, [0],
            'module_name', None)
        self.assertEqual(True, obj._is_valid_metrics_compute_frequency())

    def test_is_valid_metrics_compute_frequency_True_num(self):
        self.subject.FitCommonValidator._validate_common_args = Mock()
        obj = self.subject.FitCommonValidator(
            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
            'dep_varname', 'independent_varname', 5, 3, False, False, [0],
            'module_name', None)
        self.assertEqual(True, obj._is_valid_metrics_compute_frequency())

    def test_is_valid_metrics_compute_frequency_False_zero(self):
        self.subject.FitCommonValidator._validate_common_args = Mock()
        obj = self.subject.FitCommonValidator(
            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
            'dep_varname', 'independent_varname', 5, 0, False, False, [0],
            'module_name', None)
        self.assertEqual(False, obj._is_valid_metrics_compute_frequency())

    def test_is_valid_metrics_compute_frequency_False_greater(self):
        self.subject.FitCommonValidator._validate_common_args = Mock()
        obj = self.subject.FitCommonValidator(
            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
            'dep_varname', 'independent_varname', 5, 6, False, False, [0],
            'module_name', None)
        self.assertEqual(False, obj._is_valid_metrics_compute_frequency())


class InputValidatorTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras_validator
        self.module = madlib_keras_validator
        self.subject = self.module.InputValidator

        self.module_name = 'module'
        self.test_table = 'test_table'
        self.model_table = 'model_table'
        self.id_col = 'id_col'
        self.ind_var = 'ind_var'
        self.model_arch_table = 'model_arch_table'
        self.model_id = 2
        self.num_classes = 1598
        self.model = Sequential()
        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
                              input_shape=(1,1,1,), padding='same'))
        self.model.add(Dense(self.num_classes))
        self.classes = ['train', 'boat', 'car', 'airplane']

    def tearDown(self):
        self.module_patcher.stop()

    def test_validate_pred_type_invalid_pred_type(self):
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.validate_pred_type(
                self.module_name, 'invalid_pred_type', ['cat', 'dog'])
        self.assertIn('type', str(error.exception).lower())

    def test_validate_class_values_greater_than_1600_class_values(self):
        self.model.add(Dense(1599))
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.validate_class_values(
                self.module_name, range(1599), 'prob', self.model.to_json())
        self.assertIn('1600', str(error.exception))

    def test_validate_class_values_valid_class_values_prob(self):
        self.subject.validate_class_values(
            self.module_name, range(self.num_classes), 'prob', self.model.to_json())
        self.subject.validate_class_values(
            self.module_name, None, 'prob', self.model.to_json())

    def test_validate_class_values_valid_pred_type_valid_class_values_response(self):
        self.subject.validate_class_values(
            self.module_name, range(self.num_classes), 'response', self.model.to_json())
        self.subject.validate_class_values(
            self.module_name, None, 'response', self.model.to_json())

    def test_validate_input_shape_shapes_do_not_match(self):
        # minibatched data
        self.plpy_mock_execute.return_value = [{'shape': [1,3,32,32]}]
        with self.assertRaises(plpy.PLPYException):
            self.subject.validate_input_shape(
                self.test_table, self.ind_var, [32,32,3], 2, True)
        # non-minibatched data
        self.plpy_mock_execute.return_value = [{'n_0': 1,'n_1': 32,'n_2': 32,'n_3': 3}]
        with self.assertRaises(plpy.PLPYException):
            self.subject.validate_input_shape(
                self.test_table, self.ind_var, [32,32,3], 1)
        self.plpy_mock_execute.return_value = [{'n_0': 1,'n_1': 3}]
        with self.assertRaises(plpy.PLPYException):
            self.subject.validate_input_shape(
                self.test_table, self.ind_var, [3,32], 1)

    def test_validate_input_shape_shapes_match(self):
        # minibatched data
        self.plpy_mock_execute.return_value = [{'shape': [1,32,32,3]}]
        self.subject.validate_input_shape(
            self.test_table, self.ind_var, [32,32,3], 2, True)
        # non-minibatched data
        self.plpy_mock_execute.return_value = [{'n_0': 32,'n_1': 32,'n_2': 3}]
        self.subject.validate_input_shape(
            self.test_table, self.ind_var, [32,32,3], 1)

    def test_validate_model_arch_table_none_values(self):
        with self.assertRaises(plpy.PLPYException) as error:
            obj = self.subject.validate_model_arch_table(
                self.module_name, None, self.model_id)
        self.assertIn('null', str(error.exception).lower())

        self.module.input_tbl_valid = Mock()
        with self.assertRaises(plpy.PLPYException) as error:
            obj = self.subject.validate_model_arch_table(
                self.module_name, self.model_arch_table, None)
        self.assertIn('id', str(error.exception).lower())

    def test_validate_gpu_config_with_gpu_all_segments(self):

        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': 'all_segments'}]
        obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1])

    def test_validate_gpu_config_no_gpu_all_segments(self):

        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': 'all_segments'}]
        with self.assertRaises(plpy.PLPYException) as error:
            obj = self.subject._validate_gpu_config(self.module_name, 'foo', [0])
        self.assertIn('missing gpus', str(error.exception).lower())

    def test_validate_gpu_config_with_gpu_valid_seg_list(self):

        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': [0,1]}]
        obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1,1,0,1])

    def test_validate_gpu_config_with_gpu_invalid_seg_list(self):

        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': [0,1]}]
        with self.assertRaises(plpy.PLPYException) as error:
            obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1,0,0,1])
        self.assertIn('does not have gpu', str(error.exception).lower())

class MadlibSerializerTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras_serializer
        self.subject = madlib_keras_serializer

    def tearDown(self):
        self.module_patcher.stop()

    def test_deserialize_image_1d_weights_null_state_returns_none(self):
        self.assertEqual(None, self.subject.deserialize_as_image_1d_weights(None))

    def test_deserialize_image_1d_weights_returns_not_none(self):
        dummy_state = np.array([2,3,4,5,6], dtype=np.float32)
        res = self.subject.deserialize_as_image_1d_weights(dummy_state.tostring())
        self.assertEqual(2, res[0])
        self.assertEqual([3,4,5,6], res[1].tolist())

    def test_deserialize_nd_weights_null_input_returns_none(self):
        dummy_state = np.array([0,1,2,3,4,5,6], dtype=np.float32)
        self.assertEqual(None, self.subject.deserialize_as_nd_weights(dummy_state.tostring(), None))
        self.assertEqual(None, self.subject.deserialize_as_nd_weights(None, [1, 2, 3]))
        self.assertEqual(None, self.subject.deserialize_as_nd_weights(None, None))

    def test_deserialize_nd_weights_valid_input_returns_not_none(self):
        dummy_model_weights = np.array([3,4,5], dtype=np.float32)
        dummy_model_shape = [(2, 1, 1, 1), (1,)]
        res = self.subject.deserialize_as_nd_weights(dummy_model_weights.tostring(),
                                                     dummy_model_shape)
        self.assertEqual([[[[3.0]]], [[[4.0]]]], res[0].tolist())
        self.assertEqual([5], res[1].tolist())

    def test_deserialize_nd_weights_invalid_input_fails(self):
        # pass an invalid state with missing model weights
        invalid_model_weights = np.array([1,2], dtype=np.float32)
        dummy_model_shape = [(2, 1, 1, 1), (1,)]

        # we expect raise exception because we cannot reshape
        # model weights of size 0 into shape (2,2,3,1)
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
                                                   dummy_model_shape)

        invalid_model_weights = np.array([1,2,3,4], dtype=np.float32)
        dummy_model_shape = [(2, 2, 3, 1), (1,)]
        # we expect raise exception because we cannot reshape
        # model weights of size 2 into shape (2,2,3,1)
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
                                                   dummy_model_shape)

        invalid_model_weights = np.array([0,1,2,3,4], dtype=np.float32)
        dummy_model_shape = [(2, 1), (1,)]
        # we expect to raise exception because we cannot reshape
        # model weights of size 2 into shape (1,)
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
                                                   dummy_model_shape)

    def test_serialize_image_nd_weights_none_weights_returns_none(self):
        res = self.subject.serialize_state_with_nd_weights(0, None)
        self.assertEqual(None , res)

    def test_serialize_image_nd_weights_valid_output(self):
        res = self.subject.serialize_state_with_nd_weights(0, [np.array([1, 3]),
                                                               np.array([4,5])])
        self.assertEqual(np.array([0,1,3,4,5], dtype=np.float32).tostring(),
                         res)

    def test_serialize_image_1d_weights_none_weights_returns_none(self):
        res = self.subject.serialize_state_with_1d_weights(0, None)
        self.assertEqual(None , res)

    def test_serialize_image_1d_weights_valid_output(self):
        res = self.subject.serialize_state_with_1d_weights(0, np.array([1, 3, 4, 5]))
        self.assertEqual(np.array([0,1,3,4,5], dtype=np.float32).tostring(),
                         res)


class MadlibKerasHelperTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras_helper
        self.subject = madlib_keras_helper
        self.input_data = [32, 32, 3]

    def tearDown(self):
        self.module_patcher.stop()

    def test_expand_input_dims(self):
        self.assertEqual(np.array(self.input_data).shape, (3,))
        res = self.subject.expand_input_dims(self.input_data)
        self.assertEqual(res.shape, (1, 3))

    def test_strip_trailing_nulls_from_class_values(self):
        self.assertEqual(['cat', 'dog'],
                         self.subject.strip_trailing_nulls_from_class_values(
                ['cat', 'dog']))
        self.assertEqual([None, 'cat', 'dog'],
                         self.subject.strip_trailing_nulls_from_class_values(
                [None, 'cat', 'dog']))
        self.assertEqual([None, 'cat', 'dog'],
                         self.subject.strip_trailing_nulls_from_class_values(
                [None, 'cat', 'dog', None, None]))
        self.assertEqual(['cat', 'dog'],
                         self.subject.strip_trailing_nulls_from_class_values(
                ['cat', 'dog', None, None]))
        self.assertEqual([None],
                         self.subject.strip_trailing_nulls_from_class_values(
                [None, None]))

    def test_get_gpus_per_one_seg_gpu_gpdb(self):

        self.subject.is_platform_pg = Mock(return_value = False)

        self.plpy_mock_execute.side_effect = \
            [ [],
            [ {'hostname': 'mdw0', 'count' : 1}],
            [],
            [ {'hostname': 'mdw0', 'segment_id' : 0},
              {'hostname': 'mdw1', 'segment_id' : 1},
              {'hostname': 'mdw2', 'segment_id' : 2}
            ]]

        self.assertEqual([1,0,0], self.subject.get_accessible_gpus_for_seg(
            'schema_madlib', 2, 'foo'))

    def test_get_gpus_per_mult_seg_gpu_gpdb(self):

        self.subject.is_platform_pg = Mock(return_value = False)

        self.plpy_mock_execute.side_effect = \
            [[],
            [ {'hostname': 'mdw0', 'count' : 1}],
            [],
            [ {'hostname': 'mdw0', 'segment_id' : 0},
              {'hostname': 'mdw0', 'segment_id' : 1},
              {'hostname': 'mdw1', 'segment_id' : 2},
              {'hostname': 'mdw1', 'segment_id' : 3}
            ]]

        self.assertEqual([1,1,0,0], self.subject.get_accessible_gpus_for_seg(
            'schema_madlib', 2, 'foo'))

    def test_get_gpus_per_no_gpu_gpdb(self):

        self.subject.is_platform_pg = Mock(return_value = False)

        self.plpy_mock_execute.side_effect = [[],[],[]]
        with self.assertRaises(plpy.PLPYException) as error:
            self.subject.get_accessible_gpus_for_seg('schema_madlib', 2, 'foo')
        self.assertIn('no gpus configured on hosts', str(error.exception).lower())

    def test_get_metrics_sql_string(self):
        NaN = float('nan')
        test_metrics = [ 3.2, NaN, 0.0, None, 8.94, -6.8, 1.2, NaN, NaN ]

        py2sql = { x : str(x) for x in test_metrics if type(x) == float }
        py2sql[NaN] = "'NaN'::DOUBLE PRECISION"
        py2sql[None] = 'NULL'
        correct = [ py2sql[x] for x in test_metrics ]

        final, metrics = self.subject.get_metrics_sql_string(test_metrics)

        m = re.match("\(ARRAY\[(.*)\]\)", metrics)
        answers = m.group(1).split(', ')
        self.assertListEqual(answers, correct)
        self.assertEqual(final, '(' + correct[-1] + ')')

        # Check that postgresql parser sees this as a valid sql data type
        res = pglint('SELECT ' + final, True)
        self.assertEqual(
            res,
            (True, ''),
            "This is not valid PostgresSQL: {}".format('SELECT' + final)
        )

        # Check that postgresql parser sees this as a valid array
        res = pglint('SELECT {}[1]'.format(metrics), True)
        self.assertEqual(
            res,
            (True, ''),
            "This is not valid PostgresSQL: SELECT {}[1]".format(metrics)
        )

class MadlibKerasEvaluationMergeFinalTestCase(unittest.TestCase):
    def setUp(self):
        self.plpy_mock = Mock(spec='error')
        patches = {
            'plpy': plpy,
            'utilities.mean_std_dev_calculator': Mock()
        }

        self.plpy_mock_execute = MagicMock()
        plpy.execute = self.plpy_mock_execute

        self.module_patcher = patch.dict('sys.modules', patches)
        self.module_patcher.start()
        import madlib_keras
        self.subject = madlib_keras

        self.model = Sequential()
        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
                         input_shape=(1,1,1,), padding='same'))
        self.model.add(Flatten())

        self.compile_params = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']"
        self.model_weights = [3,4,5,6]
        self.serialized_weights = np.array(self.model_weights, dtype='float32'
                                           ).tostring()
        self.loss = 0.5947071313858032
        self.accuracy = 1.0
        self.dist_key_mapping = [0,1,2]
        self.accessible_gpus_for_seg = [0]

        #self.model.evaluate = Mock(return_value = [self.loss, self.accuracy])

        self.independent_var_real = [[[[0.5]]]] * 10
        self.dependent_var_int = [[0,1]] * 10

        # Params as bytea
        self.independent_var = np.array(self.independent_var_real, dtype=np.float32).tobytes()
        self.dependent_var = np.array(self.dependent_var_int, dtype=np.int16).tobytes()

        self.independent_var_shape = [10,1,1,1]
        self.dependent_var_shape = [10,2]
        # We test on segment 0, which has 3 buffers filled with 10 identical
        #  images each, or 30 images total
        self.total_images_per_seg = [3*len(self.dependent_var_int),20,40]

    def tearDown(self):
        self.module_patcher.stop()

    def test_internal_keras_eval_merge(self):
        image_count = self.total_images_per_seg[0]
        state1 = [3.0*self.loss, 3.0*self.accuracy, image_count]
        state1 = state1
        state2 = [2.0*self.loss, 2.0*self.accuracy, image_count+30]
        state2 = state2
        merged_state = self.subject.internal_keras_eval_merge(state1,state2)
        agg_loss = merged_state[0]
        agg_accuracy = merged_state[1]
        image_count_total = merged_state[2]

        self.assertEqual( 2*image_count+30 , image_count_total )
        self.assertAlmostEqual( 5.0*self.loss, agg_loss, 2)
        self.assertAlmostEqual( 5.0*self.accuracy, agg_accuracy, 2)

    def test_internal_keras_eval_merge_none_first(self):
        image_count = self.total_images_per_seg[0]
        input_state = [self.loss, self.accuracy, image_count]
        merged_state = self.subject.internal_keras_eval_merge(None, input_state)
        agg_loss = merged_state[0]
        agg_accuracy = merged_state[1]
        image_count_total = merged_state[2]

        self.assertEqual(image_count, image_count_total)
        self.assertAlmostEqual(self.loss, agg_loss, 2)
        self.assertAlmostEqual(self.accuracy, agg_accuracy, 2)

    def test_internal_keras_eval_merge_none_second(self):
        image_count = self.total_images_per_seg[0]
        input_state = [self.loss, self.accuracy, image_count]
        merged_state = self.subject.internal_keras_eval_merge(input_state, None)
        agg_loss = merged_state[0]
        agg_accuracy = merged_state[1]
        image_count_total = merged_state[2]

        self.assertEqual(image_count, image_count_total)
        self.assertAlmostEqual(self.loss, agg_loss, 2)
        self.assertAlmostEqual(self.accuracy, agg_accuracy, 2)

    def test_internal_keras_eval_merge_both_none(self):
        result = self.subject.internal_keras_eval_merge(None,None)
        self.assertEqual(None, result)

    def test_internal_keras_eval_final(self):
        image_count = self.total_images_per_seg[0]
        input_state = [image_count*self.loss, image_count*self.accuracy, image_count]

        output_state = self.subject.internal_keras_eval_final(input_state)
        self.assertEqual(len(output_state), 2)
        agg_loss = output_state[0]
        agg_accuracy = output_state[1]

        self.assertAlmostEqual(self.loss, agg_loss,2)
        self.assertAlmostEqual(self.accuracy, agg_accuracy,2)

    def internal_keras_eval_final_none(self):
        result = self.subject.internal_keras_eval_final(None)
        self.assertEqual(result, None)

    def test_internal_keras_eval_final_image_count_zero(self):
        input_state = [0, 0, 0]

        with self.assertRaises(plpy.PLPYException):
            result = self.subject.internal_keras_eval_final(input_state)

    def tearDown(self):
        self.module_patcher.stop()

if __name__ == '__main__':
    # Do not move any of the tensorflow imports outside of this block. This is
    # because importing tensorflow.keras.models/layers changes the `__name__`
    # variable because of which the if condition fails and the unit tests don't
    # get run

    # turn off verbose output
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    import tensorflow as tf
    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

    from tensorflow.keras.models import *
    from tensorflow.keras.layers import *
    unittest.main()
# ---------------------------------------------------------------------