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def load_dataset():
    train_dataset = h5py.File('/kaggle/input/aulas-keras/train_catvnoncat.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('/kaggle/input/aulas-keras/test_catvnoncat.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes

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def function1(n):
  a = 0
  for (i = 0; i < n; i += 1):
    for (j = 0; j < n; j += 1):
      a += 1
  return a
 
def function2(n):
  a = 0
  for (i = 0; i < n; i += 1):

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def model(input_shape):
    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!
    X_input = Input(input_shape)

    # Zero-Padding: pads the border of X_input with zeroes
    X = ZeroPadding2D((3, 3))(X_input)

    # CONV -> BN -> RELU Block applied to X
    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)

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books = ['The Lord of the Rings', 'The Hobbit', 'Harry Potter and the Chamber of Secrets', 
        'Black Beauty', 'Kane and Abel', 'To Kill a Mocking Bird', 'Gitanjali', 
        'Spring and Autumn Annals', 'Rumi Poems'] # defining a list of books

book_catalog = {} # defining a dictionary

for book in books:
    book_index = book[0] # extract the first letter (for catalog)
    if book_index not in book_catalog:
        book_catalog[book_index] = [book]

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article = '''
Asian shares skidded on Tuesday after a rout in tech stocks put Wall Street to the sword, while a 
sharp drop in oil prices and political risks in Europe pushed the dollar to 16-month highs as investors dumped 
riskier assets. MSCI’s broadest index of Asia-Pacific shares outside Japan dropped 1.7 percent to a 1-1/2 
week trough, with Australian shares sinking 1.6 percent. Japan’s Nikkei dived 3.1 percent led by losses in 
electric machinery makers and suppliers of Apple’s iphone parts. Sterling fell to $1.286 after three straight 
sessions of losses took it to the lowest since Nov.1 as there were still considerable unresolved issues with the
European Union over Brexit, British Prime Minister Theresa May said on Monday.'''

import nltk

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article = '''
Asian shares skidded on Tuesday after a rout in tech stocks put Wall Street to the sword, while a 
sharp drop in oil prices and political risks in Europe pushed the dollar to 16-month highs as investors dumped 
riskier assets. MSCI’s broadest index of Asia-Pacific shares outside Japan dropped 1.7 percent to a 1-1/2 
week trough, with Australian shares sinking 1.6 percent. Japan’s Nikkei dived 3.1 percent led by losses in 
electric machinery makers and suppliers of Apple’s iphone parts. Sterling fell to $1.286 after three straight 
sessions of losses took it to the lowest since Nov.1 as there were still considerable unresolved issues with the
European Union over Brexit, British Prime Minister Theresa May said on Monday.'''

import spacy

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import pandas as pd
url_df = pd.read_csv('Book2.csv')

def fn_split_url(x):
    list_of_tokens = x.split('/')
    bad_words = ['https:', 'http:', 'www.juniper.net', 'en_US', '']
    final_tokens = []
    s = ''
    for token in list_of_tokens:
        if token not in bad_words:

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article = '''
Asian shares skidded on Tuesday after a rout in tech stocks put Wall Street to the sword, while a 
sharp drop in oil prices and political risks in Europe pushed the dollar to 16-month highs as investors dumped 
riskier assets. MSCI’s broadest index of Asia-Pacific shares outside Japan dropped 1.7 percent to a 1-1/2 
week trough, with Australian shares sinking 1.6 percent. Japan’s Nikkei dived 3.1 percent led by losses in 
electric machinery makers and suppliers of Apple’s iphone parts. Sterling fell to $1.286 after three straight 
sessions of losses took it to the lowest since Nov.1 as there were still considerable unresolved issues with the
European Union over Brexit, British Prime Minister Theresa May said on Monday.'''

import nltk

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alist = ['h', 'e', 'a', 'd']
def insertionSort(alist):
    for i in range(1, len(alist)): # it starts from position 1 , i.e. "e" and goes till end od the string "d"
        j = i
        current_value = alist[i]
        print("Iteration: ", i)
        while (j > 0 and current_value < alist[j-1]):
            alist[j] = alist[j-1]
            j = j - 1
            print(alist)

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# Creating Spark Configuration and Spark Context-

from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("My Dataframe")
sc = SparkContext(conf = conf)

from pyspark.sql import SparkSession # To work with dataframe we need pyspark.sql
spark = SparkSession(sc) # passing Spark Context to SQL module

myRange = spark.range(1000).toDF("number")