# Written by Dr Daniel Buscombe, Marda Science LLC
# for the SandSnap Program
#
# MIT License
#
# Copyright (c) 2020-2021, Marda Science LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.


##> Release v1.4 (Aug 2021)

###===================================================
# import libraries
import gc, os, sys, shutil

###===================================================
# import and set global variables from defaults.py
from defaults import *

global IM_HEIGHT, IM_WIDTH

global NUM_EPOCHS, SHALLOW

global VALID_BATCH_SIZE, BATCH_SIZE

VALID_BATCH_SIZE = BATCH_SIZE

global MAX_LR, OPT, USE_GPU, DO_AUG, DO_STANDARDIZE


# global STOP_PATIENCE, FACTOR, MIN_DELTA, MIN_LR

# global MIN_DELTA, FACTOR, STOP_PATIENCE
##====================================================

# import tensorflow.compat.v1 as tf1
# config = tf1.ConfigProto()
# config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU
# config.log_device_placement = True  # to log device placement (on which device the operation ran)
# sess = tf1.Session(config=config)
# tf1.keras.backend.set_session(sess)

# PREDICT = False
#
# ##OS
# if PREDICT == True:
#    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

##TF/keras
if USE_GPU == True:
   ##use the first available GPU
   os.environ['CUDA_VISIBLE_DEVICES'] = '0'
else:
   ## to use the CPU (not recommended):
   os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

import numpy as np
import tensorflow as tf

# from tensorflow.keras import mixed_precision
# mixed_precision.set_global_policy('mixed_float16')

SEED=42
np.random.seed(SEED)
AUTO = tf.data.experimental.AUTOTUNE # used in tf.data.Dataset API

tf.random.set_seed(SEED)

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print('GPU name: ', tf.config.experimental.list_physical_devices('GPU'))
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

from tensorflow.keras.layers import Input, Dense, MaxPool2D, GlobalMaxPool2D
from tensorflow.keras.layers import Dropout, MaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler
from tensorflow.keras.layers import DepthwiseConv2D, Conv2D, SeparableConv2D
from tensorflow.keras.layers import BatchNormalization, Activation, concatenate

try:
    from tensorflow.keras.utils import plot_model
except:
    pass

import tensorflow.keras.backend as K
from tensorflow.keras.utils import to_categorical
import tensorflow_addons as tfa

##SKLEARN
from sklearn.preprocessing import RobustScaler #MinMaxScaler
from sklearn.metrics import confusion_matrix, classification_report

##OTHER
from PIL import Image
from glob import glob
import matplotlib.pyplot as plt
import pandas as pd
import itertools
import joblib
import random
from tempfile import TemporaryFile
import tensorflow_addons as tfa
import tqdm

from skimage.transform import AffineTransform, warp #rotate,