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import datetime

holidays = {datetime.date(2020, 1, 1): 'Yeni Yıl',
            datetime.date(2020, 4, 23): "Ulusal Egemenlik ve Çocuk Bayramı",
            datetime.date(2020, 5, 1): 'İşçi ve Emekçiler Bayramı',
            datetime.date(2020, 5, 19): "Atatürk'ü Anma, Gençlik ve Spor Bayramı",
            datetime.date(2020, 5, 23): 'Ramazan Bayramı Arife Günü',
            datetime.date(2020, 5, 24): 'Ramazan Bayramı',
            datetime.date(2020, 5, 25): 'Ramazan Bayramı ',
            datetime.date(2020, 5, 26): 'Ramazan Bayramı ',

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import re

def tr_upper(self):
    self = re.sub(r"i", "İ", self)
    self = re.sub(r"ı", "I", self)
    self = re.sub(r"ç", "Ç", self)
    self = re.sub(r"ş", "Ş", self)
    self = re.sub(r"ü", "Ü", self)
    self = re.sub(r"ğ", "Ğ", self)
    self = re.sub(r"â", "a", self)

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def seed_everything(seed: int):
    import random, os
    import numpy as np
    import torch
    
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

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contractions_dict = { "ain’t": "are not", "’s":" is", "aren’t": "are not", "can’t": "cannot", "can’t’ve": "cannot have", "‘cause": "because", 
                     "could’ve": "could have", "couldn’t": "could not", "couldn’t’ve": "could not have", "didn’t": "did not", "doesn’t": "does not", 
                     "don’t": "do not", "hadn’t": "had not", "hadn’t’ve": "had not have", "hasn’t": "has not", "haven’t": "have not", "he’d": "he would", 
                     "he’d’ve": "he would have", "he’ll": "he will", "he’ll’ve": "he will have", "how’d": "how did", "how’d’y": "how do you", "how’ll": "how will",
                     "I’d": "I would", "I’d’ve": "I would have", "I’ll": "I will", "I’ll’ve": "I will have", "I’m": "I am", "I’ve": "I have", "isn’t": "is not", 
                     "it’d": "it would", "it’d’ve": "it would have", "it’ll": "it will", "it’ll’ve": "it will have", "let’s": "let us", "ma’am": "madam", 
                     "mayn’t": "may not", "might’ve": "might have", "mightn’t": "might not", "m

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# # Turkish Cities

cities = ["Adana","Adiyaman","Afyon","Agri","Aksaray","Amasya","Ankara","Antalya","Ardahan","Artvin","Aydin","Balikesir","Bartin","Batman","Bayburt","Bilecik","Bingol","Bitlis","Bolu","Burdur","Bursa","Canakkale","Cankiri","Corum","Denizli","Diyarbakir","Duzce","Edirne","Elazig","Erzincan","Erzurum","Eskisehir","Gaziantep","Giresun","Gumushane","Hakkari","Hatay","Igdir","Isparta","Istanbul","Izmir","Kahramanmaras","Karabuk","Karaman","Kars","Kastamonu","Kayseri","Kilis","Kirikkale","Kirklareli","Kirsehir","Kocaeli","Konya","Kutahya","Malatya","Manisa","Mardin","Mersin","Mugla","Mus","Nevsehir","Nigde","Ordu","Osmaniye","Rize","Sakarya","Samsun","Sanliurfa","Siirt","Sinop","Sirnak","Sivas","Tekirdag","Tokat","Trabzon","Tunceli","Usak","Van","Yalova","Yozgat","Zonguldak"]

# # Turkish Cities Uppercase

cities_upper = ['ADANA', 'ADIYAMAN', 'AFYON', 'AGRI', 'AKSARAY', 'AMASYA', 'ANKARA', 'ANTALYA', 'ARDAHAN', 'ARTVIN', 'AYDIN', 'BALIKESIR', 'BARTIN', 'BATMAN', 'BAYBURT', 'BILECIK', 'BINGOL', '

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import matplotlib.pyplot as plt
import numpy as np
from math import ceil

def multi_convolution2d(input, filter, strides=(1, 1), padding='SAME'):
    #This is for multiple filter
    if not len(filter.shape) == 4:
        raise ValueError("The size of filter should be (filter_height, filter_width, filter_depth, number_of_filters)")
        
    if not len(input.shape) == 3:

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## MULTICLASS CROSS ENTROPY
import tensorflow as tf
import numpy as np
from sklearn.metrics import log_loss

#4 observations and 4 classes
y_true = [4,4,3,1] #Hard classes
y_true_onehot = [[0,0,0,1], [0,0,0,1], [0,0,1,0], [1,0,0,0]] #one_hot encoding
y_pred = [[0.2, 0.2, 0.2, 0.2], [0.6, 0.7, 0.1, 0.2], [0.3, 0.9, 0.1, 0.1], [0.1, 0.5, 0.7, 0.4]]
y_pred_softmax = [[0.25, 0.25, 0.25, 0.25], [0.29568115, 0.3267782, 0.17933969, 0.198201], [0.22423635, 0.40858525, 0.18358919, 0.18358919], [0.17655984, 0.26339632, 0.32171297, 0.23833084]]

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import numpy as np
import tensorflow as tf
from sklearn.datasets import fetch_california_housing
from sklearn.preprocessing import StandardScaler
import math

housing = fetch_california_housing()

print(type(housing['data']))
print(type(housing['target']))

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## BINARY CLASS CROSS ENTROPY
import tensorflow as tf
import numpy as np
from sklearn.metrics import log_loss

#4 observations and 2 classes
y_true = [0,0,0,1] #Hard classes
y_pred = [0.2, 0.2, 0.2, 0.2]
y_pred_softmax = [0.54983395, 0.54983395, 0.54983395, 0.54983395] #soft predictions
#BY HAND

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file_name    multi_hot_encoding   Labels        Categories
----------------------------------------------------------------------------------------------------
0.png        [1, 0, 0, 0, 0]      [1]           [Desert]
10.png       [1, 1, 0, 0, 1]      [1, 2]        [Desert, Mountains]
47.png       [1, 0, 0, 1, 1]      [1, 4, 5]     [Desert, Sunset, Trees]