#!/usr/bin/env python
# encoding: utf-8
#
# Copyright SAS Institute
#
#  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.
#

# NOTE: This test requires a running CAS server.  You must use an ~/.authinfo
#       file to specify your username and password.  The CAS host and port must
#       be specified using the CASHOST and CASPORT environment variables.
#       A specific protocol ('cas', 'http', 'https', or 'auto') can be set using
#       the CASPROTOCOL environment variable.

import os
import swat
import swat.utils.testing as tm
import dlpy
from swat.cas.table import CASTable
from dlpy.model import Model, Optimizer, AdamSolver, Sequence
from dlpy.sequential import Sequential
from dlpy.timeseries import TimeseriesTable
from dlpy.layers import (InputLayer, Conv2d, Conv1d, Pooling, Dense, OutputLayer,
                         Recurrent, Keypoints, BN, Res, Concat, Reshape, GlobalAveragePooling1D)
from dlpy.utils import caslibify, caslibify_context, file_exist_on_server
from dlpy.applications import Tiny_YoloV2
import unittest


class TestModel(unittest.TestCase):
    '''
    Please locate the images.sashdat file under the datasources to the DLPY_DATA_DIR.
    '''
    server_type = None
    s = None
    server_sep = '/'
    data_dir = None
    data_dir_local = None

    def setUp(self):
        swat.reset_option()
        swat.options.cas.print_messages = False
        swat.options.interactive_mode = False

        self.s = swat.CAS()
        self.server_type = tm.get_cas_host_type(self.s)
        self.server_sep = '\\'
        if self.server_type.startswith("lin") or self.server_type.startswith("osx"):
            self.server_sep = '/'

        if 'DLPY_DATA_DIR' in os.environ:
            self.data_dir = os.environ.get('DLPY_DATA_DIR')
            if self.data_dir.endswith(self.server_sep):
                self.data_dir = self.data_dir[:-1]
            self.data_dir += self.server_sep

        if 'DLPY_DATA_DIR_LOCAL' in os.environ:
            self.data_dir_local = os.environ.get('DLPY_DATA_DIR_LOCAL')
            if self.data_dir_local.endswith(self.server_sep):
                self.data_dir_local = self.data_dir_local[:-1]
            self.data_dir_local += self.server_sep

    def test_model1(self):

        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', lr=0.001)
        if r.severity > 0:
            for msg in r.messages:
                print(msg)
        self.assertTrue(r.severity <= 1)
        
        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)

    def test_model2(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_')
        self.assertTrue(r.severity == 0)

        r2 = model1.predict(data='eee')
        self.assertTrue(r2.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model3(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_')
        self.assertTrue(r.severity == 0)

        r1 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=3)
        self.assertTrue(r1.severity == 0)

        r2 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=2)
        self.assertTrue(r2.severity == 0)

        r3 = model1.predict(data='eee')
        self.assertTrue(r3.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model4(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_')
        self.assertTrue(r.severity == 0)

        r2 = model1.evaluate(data='eee')
        self.assertTrue(r2.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model5(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_')
        self.assertTrue(r.severity == 0)

        r1 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=3)
        self.assertTrue(r1.severity == 0)

        r2 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=2)
        self.assertTrue(r2.severity == 0)

        r3 = model1.evaluate(data='eee')
        self.assertTrue(r3.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model6(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)
        if r.severity > 0:
            for msg in r.messages:
                print(msg)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model7(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)

        r2 = model1.predict(data='eee', use_best_weights=True)
        self.assertTrue(r2.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model8(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)
        if r.severity > 0:
            for msg in r.messages:
                print(msg)

        r2 = model1.predict(data='eee')
        self.assertTrue(r2.severity == 0)
        if r2.severity > 0:
            for msg in r2.messages:
                print(msg)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model9(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)

        r2 = model1.evaluate(data='eee', use_best_weights=True)
        self.assertTrue(r2.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model10(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)

        r2 = model1.evaluate(data='eee')
        self.assertTrue(r2.severity == 0)

        model1.save_to_table(self.data_dir)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model11(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)

        r1 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=3)
        self.assertTrue(r1.severity == 0)

        r2 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=2)
        self.assertTrue(r2.severity == 0)

        r3 = model1.evaluate(data='eee', use_best_weights=True)
        self.assertTrue(r3.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model12(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', save_best_weights=True)
        self.assertTrue(r.severity == 0)

        r1 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=3)
        self.assertTrue(r1.severity == 0)

        r2 = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=2, save_best_weights=True)
        self.assertTrue(r2.severity == 0)

        r3 = model1.predict(data='eee', use_best_weights=True)
        self.assertTrue(r3.severity == 0)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model13(self):
        model = Sequential(self.s, model_table='simple_cnn')
        model.add(InputLayer(3, 224, 224))
        model.add(Conv2d(2, 3))
        model.add(Pooling(2))
        model.add(Dense(4))
        model.add(OutputLayer(n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        model.save_to_table(self.data_dir)

    def test_model13a(self):
        model = Sequential(self.s, model_table='simple_cnn')
        model.add(InputLayer(3, 224, 224))
        model.add(Conv2d(2, 3))
        model.add(Pooling(2))
        model.add(Dense(4))
        model.add(OutputLayer(n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        model.save_to_table(self.data_dir)

    def test_model13b(self):
        model = Sequential(self.s, model_table='simple_cnn')
        model.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model.add(layer=OutputLayer(n=10, full_connect=False))
        self.assertTrue(model.summary.loc[1, 'Number of Parameters'] == (0, 0))

        model1 = Sequential(self.s, model_table='simple_cnn')
        model1.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model1.add(layer=OutputLayer(n=10, full_connect=True))
        self.assertTrue(model1.summary.loc[1, 'Number of Parameters'] == (1000, 10))

        model2 = Sequential(self.s, model_table='Simple_CNN')
        model2.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model2.add(layer=OutputLayer(n=10, full_connect=True, include_bias=False))
        self.assertTrue(model2.summary.loc[1, 'Number of Parameters'] == (1000, 0))

        model3 = Sequential(self.s, model_table='Simple_CNN')
        model3.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model3.add(layer=Conv2d(4, 3))
        model3.add(layer=OutputLayer(n=10))
        self.assertTrue(model3.summary.loc[2, 'Number of Parameters'] == (4000, 10))

        model4 = Sequential(self.s, model_table='Simple_CNN')
        model4.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model4.add(layer=Conv2d(4, 3))
        model4.add(layer=OutputLayer(n=10, full_connect=False))
        self.assertTrue(model4.summary.loc[2, 'Number of Parameters'] == (0, 0))

    def test_model14(self):
        model = Sequential(self.s, model_table='Simple_CNN')
        model.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model.add(layer=OutputLayer())
        model.summary

    def test_model15(self):
        model = Sequential(self.s, model_table='Simple_CNN')
        model.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model.add(layer=Keypoints())
        self.assertTrue(model.summary.loc[1, 'Number of Parameters'] == (0, 0))

    def test_model16(self):
        model = Sequential(self.s, model_table='Simple_CNN')
        model.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model.add(layer=Keypoints(n=10, include_bias=False))
        self.assertTrue(model.summary.loc[1, 'Number of Parameters'] == (1000, 0))

    def test_model16(self):
        model = Sequential(self.s, model_table='Simple_CNN')
        model.add(layer=InputLayer(n_channels=1, height=10, width=10))
        model.add(layer=Keypoints(n=10))
        self.assertTrue(model.summary.loc[1, 'Number of Parameters'] == (1000, 10))

    def test_model18(self):
        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', max_epochs=1)
        self.assertTrue(r.severity == 0)

        model1.save_weights_csv(self.data_dir)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_evaluate_obj_det(self):

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path = self.data_dir + 'evaluate_obj_det_det.sashdat', task = 'load')

        self.s.table.loadtable(caslib = caslib,
                               casout = {'name': 'evaluate_obj_det_det', 'replace': True},
                               path = path)

        self.s.table.loadtable(caslib = caslib,
                               casout = {'name': 'evaluate_obj_det_gt', 'replace': True},
                               path = 'evaluate_obj_det_gt.sashdat')
        yolo_anchors = (5.9838598901098905,
                        3.4326923076923075,
                        2.184993862520458,
                        1.9841448445171848,
                        1.0261752136752136,
                        1.2277777777777779)
        yolo_model = Tiny_YoloV2(self.s, grid_number = 17, scale = 1.0 / 255,
                                 n_classes = 1, height = 544, width = 544,
                                 predictions_per_grid = 3,
                                 anchors = yolo_anchors,
                                 max_boxes = 100,
                                 coord_type = 'yolo',
                                 max_label_per_image = 100,
                                 class_scale = 1.0,
                                 coord_scale = 2.0,
                                 prediction_not_a_object_scale = 1,
                                 object_scale = 5,
                                 detection_threshold = 0.05,
                                 iou_threshold = 0.2)

        metrics = yolo_model.evaluate_object_detection(ground_truth = 'evaluate_obj_det_gt', coord_type = 'yolo',
                                                       detection_data = 'evaluate_obj_det_det', iou_thresholds=0.5)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)
        
    def test_model_forecast1(self):
        
        import datetime
        try:
            import pandas as pd
        except:
            unittest.TestCase.skipTest(self, "pandas not found in the libraries") 
        import numpy as np
            
        filename1 = os.path.join(os.path.dirname(__file__), 'datasources', 'timeseries_exp1.csv')
        importoptions1 = dict(filetype='delimited', delimiter=',')
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        self.table1 = TimeseriesTable.from_localfile(self.s, filename1, importoptions=importoptions1)
        self.table1.timeseries_formatting(timeid='datetime',
                                  timeseries=['series', 'covar'],
                                  timeid_informat='ANYDTDTM19.',
                                  timeid_format='DATETIME19.')
        self.table1.timeseries_accumlation(acc_interval='day',
                                           groupby=['id1var', 'id2var'])
        self.table1.prepare_subsequences(seq_len=2,
                                         target='series',
                                         predictor_timeseries=['series'],
                                         missing_handling='drop')
        
        valid_start = datetime.date(2015, 1, 4)
        test_start = datetime.date(2015, 1, 7)
        
        traintbl, validtbl, testtbl = self.table1.timeseries_partition(
                validation_start=valid_start, testing_start=test_start)
        
        model1 = Sequential(self.s, model_table='lstm_rnn')
        model1.add(InputLayer(std='STD'))
        model1.add(Recurrent(rnn_type='LSTM', output_type='encoding', n=15, reversed_=False))
        model1.add(OutputLayer(act='IDENTITY'))
        
        optimizer = Optimizer(algorithm=AdamSolver(learning_rate=0.01), mini_batch_size=32, 
                              seed=1234, max_epochs=10)                    
        seq_spec  = Sequence(**traintbl.sequence_opt)
        result = model1.fit(traintbl, valid_table=validtbl, optimizer=optimizer, 
                            sequence=seq_spec, **traintbl.inputs_target)
        
        self.assertTrue(result.severity == 0)
        
        resulttbl1 = model1.forecast(horizon=1)
        self.assertTrue(isinstance(resulttbl1, CASTable))
        self.assertTrue(resulttbl1.shape[0]==15)
        
        local_resulttbl1 = resulttbl1.to_frame()
        unique_time = local_resulttbl1.datetime.unique()
        self.assertTrue(len(unique_time)==1)
        self.assertTrue(pd.Timestamp(unique_time[0])==datetime.datetime(2015,1,7))

        resulttbl2 = model1.forecast(horizon=3)
        self.assertTrue(isinstance(resulttbl2, CASTable))
        self.assertTrue(resulttbl2.shape[0]==45)
        
        local_resulttbl2 = resulttbl2.to_frame()
        local_resulttbl2.sort_values(by=['id1var', 'id2var', 'datetime'], inplace=True)
        unique_time = local_resulttbl2.datetime.unique()
        self.assertTrue(len(unique_time)==3)
        for i in range(3):
            self.assertTrue(pd.Timestamp(unique_time[i])==datetime.datetime(2015,1,7+i))
        
        series_lag1 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag1'].values
                                           
        series_lag2 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag2'].values
        
        DL_Pred = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             '_DL_Pred_'].values
                                       
        self.assertTrue(np.array_equal(series_lag1[1:3], DL_Pred[0:2]))
        self.assertTrue(series_lag2[2]==DL_Pred[0])        

    def test_model_forecast2(self):
        
        import datetime
        try:
            import pandas as pd
        except:
            unittest.TestCase.skipTest(self, "pandas not found in the libraries") 
        import numpy as np
            
        filename1 = os.path.join(os.path.dirname(__file__), 'datasources', 'timeseries_exp1.csv')
        importoptions1 = dict(filetype='delimited', delimiter=',')
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        self.table2 = TimeseriesTable.from_localfile(self.s, filename1, importoptions=importoptions1)
        self.table2.timeseries_formatting(timeid='datetime',
                                  timeseries=['series', 'covar'],
                                  timeid_informat='ANYDTDTM19.',
                                  timeid_format='DATETIME19.')
        self.table2.timeseries_accumlation(acc_interval='day',
                                           groupby=['id1var', 'id2var'])
        self.table2.prepare_subsequences(seq_len=2,
                                         target='series',
                                         predictor_timeseries=['series', 'covar'],
                                         missing_handling='drop')
        
        valid_start = datetime.date(2015, 1, 4)
        test_start = datetime.date(2015, 1, 7)
        
        traintbl, validtbl, testtbl = self.table2.timeseries_partition(
                validation_start=valid_start, testing_start=test_start)
        
        model1 = Sequential(self.s, model_table='lstm_rnn')
        model1.add(InputLayer(std='STD'))
        model1.add(Recurrent(rnn_type='LSTM', output_type='encoding', n=15, reversed_=False))
        model1.add(OutputLayer(act='IDENTITY'))
        
        optimizer = Optimizer(algorithm=AdamSolver(learning_rate=0.01), mini_batch_size=32, 
                              seed=1234, max_epochs=10)                    
        seq_spec  = Sequence(**traintbl.sequence_opt)
        result = model1.fit(traintbl, valid_table=validtbl, optimizer=optimizer, 
                            sequence=seq_spec, **traintbl.inputs_target)
        
        self.assertTrue(result.severity == 0)
        
        resulttbl1 = model1.forecast(testtbl, horizon=1)
        self.assertTrue(isinstance(resulttbl1, CASTable))
        self.assertTrue(resulttbl1.shape[0]==testtbl.shape[0])
        
        local_resulttbl1 = resulttbl1.to_frame()
        unique_time = local_resulttbl1.datetime.unique()
        self.assertTrue(len(unique_time)==4)
        for i in range(4):
            self.assertTrue(pd.Timestamp(unique_time[i])==datetime.datetime(2015,1,7+i))

        resulttbl2 = model1.forecast(testtbl, horizon=3)
        self.assertTrue(isinstance(resulttbl2, CASTable))
        self.assertTrue(resulttbl2.shape[0]==45)
        
        local_resulttbl2 = resulttbl2.to_frame()
        local_resulttbl2.sort_values(by=['id1var', 'id2var', 'datetime'], inplace=True)
        unique_time = local_resulttbl2.datetime.unique()
        self.assertTrue(len(unique_time)==3)
        for i in range(3):
            self.assertTrue(pd.Timestamp(unique_time[i])==datetime.datetime(2015,1,7+i))
        
        series_lag1 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag1'].values
                                           
        series_lag2 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag2'].values
        
        DL_Pred = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             '_DL_Pred_'].values
                                       
        self.assertTrue(np.array_equal(series_lag1[1:3], DL_Pred[0:2]))
        self.assertTrue(series_lag2[2]==DL_Pred[0])        

    def test_model_forecast3(self):
        
        import datetime
        try:
            import pandas as pd
        except:
            unittest.TestCase.skipTest(self, "pandas not found in the libraries") 
        import numpy as np
            
        filename1 = os.path.join(os.path.dirname(__file__), 'datasources', 'timeseries_exp1.csv')
        importoptions1 = dict(filetype='delimited', delimiter=',')
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        self.table3 = TimeseriesTable.from_localfile(self.s, filename1, importoptions=importoptions1)
        self.table3.timeseries_formatting(timeid='datetime',
                                  timeseries=['series', 'covar'],
                                  timeid_informat='ANYDTDTM19.',
                                  timeid_format='DATETIME19.')
        self.table3.timeseries_accumlation(acc_interval='day',
                                           groupby=['id1var', 'id2var'])
        self.table3.prepare_subsequences(seq_len=2,
                                         target='series',
                                         predictor_timeseries=['series', 'covar'],
                                         missing_handling='drop')
        
        valid_start = datetime.date(2015, 1, 4)
        test_start = datetime.date(2015, 1, 7)
        
        traintbl, validtbl, testtbl = self.table3.timeseries_partition(
                validation_start=valid_start, testing_start=test_start)
        
        sascode = '''
        data {};
        set {};
        drop series_lag1;
        run;
        '''.format(validtbl.name, validtbl.name)
        
        self.s.retrieve('dataStep.runCode', _messagelevel='error', code=sascode)
        
        sascode = '''
        data {};
        set {};
        drop series_lag1;
        run;
        '''.format(testtbl.name, testtbl.name)
        
        self.s.retrieve('dataStep.runCode', _messagelevel='error', code=sascode)
        
        model1 = Sequential(self.s, model_table='lstm_rnn')
        model1.add(InputLayer(std='STD'))
        model1.add(Recurrent(rnn_type='LSTM', output_type='encoding', n=15, reversed_=False))
        model1.add(OutputLayer(act='IDENTITY'))
        
        optimizer = Optimizer(algorithm=AdamSolver(learning_rate=0.01), mini_batch_size=32, 
                              seed=1234, max_epochs=10)                    
        seq_spec  = Sequence(**traintbl.sequence_opt)
        result = model1.fit(traintbl, optimizer=optimizer, 
                            sequence=seq_spec, **traintbl.inputs_target)
        
        self.assertTrue(result.severity == 0)
        
        resulttbl1 = model1.forecast(validtbl, horizon=1)
        self.assertTrue(isinstance(resulttbl1, CASTable))
        self.assertTrue(resulttbl1.shape[0]==15)
        
        local_resulttbl1 = resulttbl1.to_frame()
        unique_time = local_resulttbl1.datetime.unique()
        self.assertTrue(len(unique_time)==1)
        self.assertTrue(pd.Timestamp(unique_time[0])==datetime.datetime(2015,1,4))

        resulttbl2 = model1.forecast(validtbl, horizon=3)
        self.assertTrue(isinstance(resulttbl2, CASTable))
        self.assertTrue(resulttbl2.shape[0]==45)
        
        local_resulttbl2 = resulttbl2.to_frame()
        local_resulttbl2.sort_values(by=['id1var', 'id2var', 'datetime'], inplace=True)
        unique_time = local_resulttbl2.datetime.unique()
        self.assertTrue(len(unique_time)==3)
        for i in range(3):
            self.assertTrue(pd.Timestamp(unique_time[i])==datetime.datetime(2015,1,4+i))
        
        series_lag1 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag1'].values
                                           
        series_lag2 = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             'series_lag2'].values
        
        DL_Pred = local_resulttbl2.loc[(local_resulttbl2.id1var==1) & (local_resulttbl2.id2var==1), 
                             '_DL_Pred_'].values
                                       
        self.assertTrue(np.array_equal(series_lag1[1:3], DL_Pred[0:2]))
        self.assertTrue(series_lag2[2]==DL_Pred[0]) 
        
        with self.assertRaises(RuntimeError):
            resulttbl3 = model1.forecast(testtbl, horizon=3)
        
    def test_load_reshape_detection(self):
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
        yolo_model = Model(self.s)
        yolo_model.load(self.data_dir + 'YOLOV2_MULTISIZE.sashdat')
        model_df = self.s.fetch(table = dict(name = yolo_model.model_name,
                                             where = '_DLKey0_ eq "detection1" or _DLKey0_ eq "reshape1"'),
                                to = 50).Fetch
        anchors_5 = model_df['_DLNumVal_'][model_df['_DLKey1_'] == 'detectionopts.anchors.8'].tolist()[0]
        self.assertAlmostEqual(anchors_5, 1.0907, 4)
        depth = model_df['_DLNumVal_'][model_df['_DLKey1_'] == 'reshapeopts.depth'].tolist()[0]
        self.assertEqual(depth, 256)

    def test_plot_ticks(self):

        model1 = Sequential(self.s, model_table='Simple_CNN1')
        model1.add(InputLayer(3, 224, 224))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Conv2d(8, 7))
        model1.add(Pooling(2))
        model1.add(Dense(16))
        model1.add(OutputLayer(act='softmax', n=2))

        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")

        caslib, path, tmp_caslib = caslibify(self.s, path=self.data_dir+'images.sashdat', task='load')

        self.s.table.loadtable(caslib=caslib,
                               casout={'name': 'eee', 'replace': True},
                               path=path)

        r = model1.fit(data='eee', inputs='_image_', target='_label_', lr=0.001, max_epochs=5)
        
        # Test default tick_frequency value of 1
        ax = model1.plot_training_history()
        self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs)

        # Test even
        tick_frequency = 2
        ax = model1.plot_training_history(tick_frequency=tick_frequency)
        self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)

        # Test odd
        tick_frequency = 3
        ax = model1.plot_training_history(tick_frequency=tick_frequency)
        self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)

        # Test max
        tick_frequency = model1.n_epochs
        ax = model1.plot_training_history(tick_frequency=tick_frequency)
        self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs // tick_frequency + 1)
        
        # Test 0 
        tick_frequency = 0
        ax = model1.plot_training_history(tick_frequency=tick_frequency)
        self.assertEqual(len(ax.xaxis.majorTicks), model1.n_epochs)

        if (caslib is not None) and tmp_caslib:
            self.s.retrieve('table.dropcaslib', message_level = 'error', caslib = caslib)

    def test_stride(self):
        model = Sequential(self.s, model_table = 'Simple_CNN_3classes_cropped')
        model.add(InputLayer(1, width = 36, height = 144, #offsets = myimage.channel_means,
                             name = 'input1',
                             random_mutation = 'random',
                             random_flip = 'HV'))

        model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(64, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Pooling(height = 2, width = 2, stride_vertical = 2, stride_horizontal = 1, pool = 'max'))  # 72, 36

        model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(128, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Pooling(height = 2, width = 2, stride_vertical = 2, stride_horizontal = 1, pool = 'max'))  # 36*36

        model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(256, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Pooling(2, pool = 'max'))  # 18 * 18

        model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(512, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Pooling(2, pool = 'max'))  # 9 * 9

        model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Conv2d(1024, 3, 3, include_bias = False, act = 'identity'))
        model.add(BN(act = 'relu'))
        model.add(Pooling(9))

        model.add(Dense(256, dropout = 0.5))
        model.add(OutputLayer(act = 'softmax', n = 3, name = 'output1'))
        self.assertEqual(model.summary['Output Size'].values[-3], (1, 1, 1024))
        model.print_summary()
        # 2d print summary numerical check
        self.assertEqual(model.summary.iloc[1, -1], 2985984)

    def test_heat_map_analysis(self):
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR is not set in the environment variables')
        if not file_exist_on_server(self.s, self.data_dir + 'ResNet-50-model.caffemodel.h5'):
            unittest.TestCase.skipTest(self, "File, {}, not found.".format(self.data_dir
                                                                           + 'ResNet-50-model.caffemodel.h5'))

        from dlpy.applications import ResNet50_Caffe
        from dlpy.images import ImageTable

        pre_train_weight_file = os.path.join(self.data_dir, 'ResNet-50-model.caffemodel.h5')
        my_im = ImageTable.load_files(self.s, self.data_dir+'giraffe_dolphin_small')
        my_im_r = my_im.resize(width=224, inplace=False)

        model = ResNet50_Caffe(self.s, model_table='ResNet50_Caffe',
                               n_classes=2, n_channels=3, width=224, height=224, scale=1,
                               random_flip='none', random_crop='none',
                               offsets=my_im_r.channel_means, pre_trained_weights=True,
                               pre_trained_weights_file=pre_train_weight_file,
                               include_top=False)
        model.fit(data=my_im_r, mini_batch_size=1, max_epochs=1)
        model.heat_map_analysis(data=my_im_r, mask_width=None, mask_height=None, step_size=None,
                                 max_display=1)

        self.assertRaises(ValueError, lambda:model.heat_map_analysis(mask_width=56, mask_height=56,
                           step_size=8, display=False))

        self.assertRaises(ValueError, lambda:model.heat_map_analysis(data=my_im, mask_width=56,
                           mask_height=56, step_size=8, display=False))

        try:
            from numpy import array
        except:
            unittest.TestCase.skipTest(self, 'numpy is not installed')
        self.assertRaises(ValueError, lambda:model.heat_map_analysis(data=array([]), mask_width=56,
                           mask_height=56, step_size=8, display=False))

    def test_load_padding(self):
        if self.data_dir is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
        model5 = Model(self.s)
        model5.load(path = self.data_dir + 'vgg16.sashdat')

    def test_conv1d_model(self):
        # a model from https://blog.goodaudience.com/introduction-to-1d-convolutional-neural-networks-in-keras-for-time-sequences-3a7ff801a2cf
        Conv1D = Conv1d
        MaxPooling1D=Pooling
        model_m = Sequential(self.s)
        model_m.add(InputLayer(width=80*3, height=1, n_channels=1))
        model_m.add(Conv1D(100, 10, act='relu'))
        model_m.add(Conv1D(100, 10, act='relu'))
        model_m.add(MaxPooling1D(3))
        model_m.add(Conv1D(160, 10, act='relu'))
        model_m.add(Conv1D(160, 10, act='relu'))
        model_m.add(GlobalAveragePooling1D(dropout=0.5))
        model_m.add(OutputLayer(n=6, act='softmax'))
        # use assertEqual to check whether the layer output size matches the expected value for MaxPooling1D
        self.assertEqual(model_m.layers[3].output_size, (1, 80, 100))
        model_m.print_summary()
        # 1d print summary numerical check
        self.assertEqual(model_m.summary.iloc[1, -1], 240000)

    def test_load_weights_attr(self):
        model = Model(self.s)
        model.load(path=self.data_dir+'Simple_CNN1.sashdat')
        # load_weights_attr table from server; expect to be clean
        model.load_weights_attr(self.data_dir+'Simple_CNN1_weights_attr.sashdat')

    def test_mobilenetv2(self):
        try:
            import onnx
            from dlpy.model_conversion.onnx_transforms import (Transformer, OpTypePattern,
                                                               ConstToInitializer,
                                                               InitReshape, InitUnsqueeze,
                                                               FuseMulAddBN)
            from dlpy.model_conversion.onnx_graph import OnnxGraph
            from onnx import helper, numpy_helper
        except:
            unittest.TestCase.skipTest(self, 'onnx package not found')

        from dlpy.model import Model

        if self.data_dir_local is None:
            unittest.TestCase.skipTest(self, "DLPY_DATA_DIR_LOCAL is not set in the environment variables")


        path = os.path.join(self.data_dir_local, 'mobilenetv2-1.0.onnx')

        onnx_model = onnx.load_model(path)
        model1 = Model.from_onnx_model(self.s,
                                       onnx_model,
                                       output_model_table='mobilenetv2',
                                       offsets=255*[0.485, 0.456, 0.406],
                                       norm_stds=255*[0.229, 0.224, 0.225])

    def test_model_crnn_bug(self):
        model = Sequential(self.s, model_table='crnn')
        model.add(InputLayer(3,256,16))
        model.add(Reshape(height=16,width=256,depth=3))

        model.add(Conv2d(64,3,3,stride=1,padding=1))                # size = 16x256x64
        model.add(Pooling(2,2,2))                                   # size = 8x128x64

        model.add(Conv2d(128,3,3,stride=1,padding=1))               # size = 8x128x128
        model.add(Pooling(2,2,2))                                   # size = 4x64x128

        model.add(Conv2d(256,3,3,stride=1,padding=1,act='IDENTITY')) # size = 4x64x256
        model.add(BN(act='RELU'))                   # size = 4x64x256

        model.add(Conv2d(256,3,3,stride=1,padding=1))              # size = 4x64x256


        model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2))



        #, padding=1))           #  size = 2x64x256
        #model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,))           # size = 2x64x256

        model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 2x64x512
        model.add(BN(act='RELU'))

        model.add(Conv2d(512,3,3,stride=1,padding=1))              # size = 2x64x512
        model.add(Pooling(1,2,stride_horizontal=1, stride_vertical=2)) #, padding=1))           # size = 1x64x512
        #model.add(Pooling(1,2,stride=2,stride_horizontal=1, stride_vertical=2,))           # size = 1x64x512

        model.add(Conv2d(512,3,3,stride=1,padding=1, act='IDENTITY')) # size = 1x64x512
        model.add(BN(act='RELU'))

        model.add(Reshape(order='DWH',width=64, height=512, depth=1))

        model.add(Recurrent(512,output_type='SAMELENGTH'))

        model.add(OutputLayer(error='CTC'))

        model.print_summary()

    def tearDown(self):
        # tear down tests
        try:
            self.s.terminate()
        except swat.SWATError:
            pass
        del self.s
        swat.reset_option()


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

pandas.Timestamp