Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts

Tuesday 14 November 2023

round(3 / 2) round(5 / 2)

 The round() function is used to round a number to the nearest integer. Let's calculate the results:


round(3 / 2) is equal to round(1.5), and when rounded to the nearest integer, it becomes 2.

round(5 / 2) is equal to round(2.5), and when rounded to the nearest integer, it becomes 2.

So, the results are:


round(3 / 2) equals 2.

round(5 / 2) equals 2.


Why does round(5 / 2) return 2 instead of 3? The issue here is that Python’s round method implements banker’s rounding, where all half values will be rounded to the closest even number. 

The most difficult Python questions:

  • What is the Global Interpreter Lock (GIL)? Why is it important?
  • Define self in Python?
  • What is pickling and unpickling in Python?
  • How do you reverse a list in Python?
  • What does break and continue do in Python?
  • Can break and continue be used together?
  • Explain generators vs iterators.
  • How do you access a module written in Python from C?
  • What is the difference between a List and a Tuple?
  • What is __init__() in Python?
  • What is the difference between a mutable data type and an immutable data type?
  • Explain List, Dictionary, and Tuple comprehension with an example.
  • What is monkey patching in Python?
  • What is the Python “with” statement designed for?
  • Why use else in try/except construct in Python?
  • What are the advantages of NumPy over regular Python lists?
  • What is the difference between merge, join and concatenate?
  • How do you identify and deal with missing values?
  • Which all Python libraries have you used for visualization?
  • What is the most difficult Python question you have ever encountered?

Python: Lists vs. Tuples vs. Sets vs. Dictionaries

 lists, tuples, sets, and dictionaries in Python based on various characteristics:


Mutability:

Lists: Mutable. You can modify, add, or remove elements after creation.

Tuples: Immutable. Once created, elements cannot be changed.

Sets: Mutable. You can add or remove elements, but each element must be unique.

Dictionaries: Mutable. You can add, modify, or remove key-value pairs.


Ordering:

Lists: Ordered. Elements are stored in a specific order and can be accessed by index.

Tuples: Ordered. Similar to lists, elements have a specific order.

Sets: Unordered. Elements have no specific order, and you cannot access them by index.

Dictionaries: Prior to Python 3.7, dictionaries were unordered. From Python 3.7 onwards, dictionaries maintain insertion order.


Duplicates:

Lists: Can contain duplicate elements.

Tuples: Can contain duplicate elements.

Sets: Cannot contain duplicate elements.

Dictionaries: Keys must be unique.


Syntax:

Lists: Defined using square brackets [].

Tuples: Defined using parentheses ().

Sets: Defined using curly braces {}.

Dictionaries: Defined using curly braces {} with key-value pairs separated by colons.


Use Cases:

Lists: Use when you need a mutable, ordered collection with the possibility of duplicate elements.

Tuples: Use when you need an immutable, ordered collection. Suitable for situations where data should not be changed.

Sets: Use when you need an unordered collection of unique elements and order doesn't matter.

Dictionaries: Use when you need a mutable collection of key-value pairs, providing fast lookup based on keys.


Example:

my_list = [1, 2, 3]

my_tuple = (4, 5, 6)

my_set = {7, 8, 9}

my_dict = {'a': 10, 'b': 11, 'c': 12}




Object-Oriented Python: Inheritance and Encapsulation

 


What you'll learn

How to architect larger programs using object-oriented principles

Re-use parts of classes using inheritance

Encapsulate relevant information and methods in a class


There are 4 modules in this course

Code and run your first python program in minutes without installing anything!

This course is designed for learners with limited coding experience, providing a solid foundation of not just python, but core Computer Science topics that can be transferred to other languages. The modules in this course cover inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 3 courses in this specialization is recommended.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.

Join free - Object-Oriented Python: Inheritance and Encapsulation

Python Coding challenge - Day 69 | What is the output of the following Python code?

 


The above code of the list my_list using slicing and assigns a new set of values [7, 8, 9] to the sliced portion. Here's a step-by-step breakdown:

my_list = [1, 2, 3, 4, 5]

This initializes a list with the values [1, 2, 3, 4, 5].

my_list[1:3] = [7, 8, 9]

This slices the elements at index 1 and 2 (exclusive) of my_list and replaces them with the values [7, 8, 9]. After this line executes, my_list becomes [1, 7, 8, 9, 4, 5].

print(my_list)

This prints the modified list:

[1, 7, 8, 9, 4, 5]

So, the final output is [1, 7, 8, 9, 4, 5].

Monday 13 November 2023

Python Coding challenge - Day 68 | What is the output of the following Python code?

 




The expression 3 * 2 ** 3 involves exponentiation and multiplication. The exponentiation operator ** has higher precedence than multiplication.

Let's break it down step by step:

3. 2∗∗32∗∗3 is 2×2×22×2×2, which equals 88.

4. 3×83×8 is 2424.

So, the output of the code: print(3 * 2 ** 3)

24

Do you know the reason ?

 


The all function returns True if all elements of the iterable are true (or if the iterable is empty). If any element is false, it returns False.

In the case of an empty iterable, such as an empty list ([]), the all function returns True because there are no elements that are false.

So, when you execute print(all([])), it will output: True


The any function returns True if at least one element of the iterable is true. If the iterable is empty, it returns False because there are no elements to evaluate.

In the case of an empty iterable, such as an empty list ([]), the any function returns False.

So, when you execute print(any([])), it will output: False

A simple Python code for checking whether a given number is prime or not

 


def is_prime(number):

    # Check if the number is less than 2

    if number < 2:

        return False

    

    # Check for factors from 2 to the square root of the number

    for i in range(2, int(number**0.5) + 1):

        if number % i == 0:

            # If a factor is found, the number is not prime

            return False

    

    # If no factors are found, the number is prime

    return True


# Example usage

num = int(input("Enter a number: "))

if is_prime(num):

    print(f"{num} is a prime number.")

else:

    print(f"{num} is not a prime number.")


Explanation:


The is_prime function takes an integer number as an argument and returns True if the number is prime, and False otherwise.

The function first checks if the number is less than 2. If it is, the number is not prime, as prime numbers are defined as greater than 1.

It then uses a for loop to iterate over the range from 2 to the square root of the number (rounded up to the nearest integer). This is an optimization, as factors of a number larger than its square root must pair with factors smaller than its square root.

Inside the loop, it checks if the number is divisible evenly by the current value of i. If it is, then the number is not prime, and the function returns False.

If no factors are found in the loop, the function returns True, indicating that the number is prime.

Finally, an example usage of the function is shown, where the user is prompted to enter a number, and the program prints whether it is prime or not based on the result of the is_prime function.

Count number of files and directories

 


import os

# Path IN which we have to count files and directories

PATH = 'E:\elements'   # Give your path here


fileCount = 0

dirCount = 0


for root, dirs, files in os.walk(PATH):

    print('Looking in:',root)

    for directories in dirs:

        dirCount += 1

    for Files in files:

        fileCount += 1

#clcoding.com

print('Number of files',fileCount)

print('Number of Directories',dirCount)

print('Total:',(dirCount + fileCount))

Sunday 12 November 2023

Introduction to Python

 


What you'll learn

Uses of Python

Python variables and input

Python Decisions and Looping


About this Guided Project

Learning Python gives the programmer a wide variety of career paths to choose from. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things.

In this tutorial you will create a guessing game application that pits the computer against the user. You will create variables, decision constructs, and loops in python to create the game.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  • Task 1: How Python is Used

  • Task 2: Python Input and variables

  • Task3: Python Decisions

  • Challenge Task: Python Input and Decisions

  • Challenge Solution: Python Input and Decisions

  • Task 4: Python Loops

  • Task 5: Python Functions

  • Challenge Task: Python While Loops and Functions

  • Challenge Solution: Python While Loops and Functions

Join - Introduction to Python

Python Coding challenge - Day 67 | What is the output of the following Python code?

 


In Python, the True Boolean value is equivalent to the integer 1, and False is equivalent to 0. Therefore, when you multiply True by any number, it's like multiplying 1 by that number.

In this code: print(True * 10)

It will output 10 because True is equivalent to 1, and 1 * 10 equals 10.

Saturday 11 November 2023

Happy Diwali using Python Turtle

 


import turtle

s = turtle.Screen()

t = turtle.Turtle()

def move_to(x,y):

    t.penup()

    t.goto(x,y)

    t.pendown()

def draw_rectangle(a,b):

    t.begin_fill()

    t.forward(a)

    t.left(90)

    t.forward(b)

    t.left(90)

    t.forward(a)

    t.left(90)

    t.forward(b)

    t.end_fill()

    t.speed(10)

t.color("red")

move_to(-500,200)

draw_rectangle(10,100)

move_to(-490,250)

t.left(90)

draw_rectangle(80,10)

move_to(-410,200)

t.left(90)

draw_rectangle(10,100)

move_to(-380,200)

t.left(60)

t.color("yellow")

draw_rectangle(10,122)

move_to(-275,198)

t.left(145)

draw_rectangle(10,110)

move_to(-350,230)

t.left(335)

draw_rectangle(10,63)

move_to(-240,198)

t.left(270)

t.color("green")

draw_rectangle(100,10)

move_to(-240,288)

draw_rectangle(50,10)

move_to(-190,288)

draw_rectangle(40,10)

move_to(-190,248)

draw_rectangle(50,10)

move_to(-160,198)

t.color("violet")

draw_rectangle(100,10)

move_to(-160,288)

draw_rectangle(50,10)

move_to(-110,288)

draw_rectangle(40,10)

move_to(-110,248)

draw_rectangle(50,10)

move_to(-80,198)

t.left(320)

t.color("orange")

draw_rectangle(120,10)

move_to(-100,295)

draw_rectangle(68,10)

move_to(-500,80)

t.left(40)

t.color("blue")

draw_rectangle(100,10)

t.left(180)

move_to(-490,70)

draw_rectangle(50,10)

t.left(90)

t.fd(50)

t.right(90)

draw_rectangle(80,10)

t.left(90)

t.fd(80)

t.left(270)

draw_rectangle(50,10)

move_to(-400,80)

t.left(180)

t.color("pink")

draw_rectangle(100,10)

move_to(-220,70)

t.left(60)

t.color("brown")

draw_rectangle(100,10)

t.left(300)

move_to(-370,70)

t.right(152)

draw_rectangle(100,10)

t.left(152)

move_to(-290,10)

t.right(45)

draw_rectangle(40,10)

t.right(3)

draw_rectangle(40,10)

move_to(-200,-15)

t.left(200)

t.color("darkgreen")

draw_rectangle(10,110)

move_to(-95,-20)

t.left(145)

draw_rectangle(10,114)

move_to(-175,25)

t.left(333)

draw_rectangle(10,63)

move_to(-70,79)

t.left(90)

t.color("skyblue")

draw_rectangle(101,10)

move_to(-60,-22)

t.left(180)

draw_rectangle(80,10)

move_to(50,-22)

t.left(180)

t.color("black")

draw_rectangle(100,10)

move_to(300,150)

t.color("red")

angle=0

for i in range(20):

    t.fd(50)

    move_to(300,150)

    angle+=18

    t.left(angle)

move_to(450,150)

t.color("blue")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(450,150)

move_to(375,300)

t.color("green")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(375,300)

move_to(375,-300)

t.color("black")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(375,-300)

move_to(150,-150)

t.color("violet")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(150,-150)

move_to(450,-150)

t.color("brown")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(450,-150)

move_to(-200,-300)

t.color("green")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-200,-300)

move_to(125,0)

t.color("yellow")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(125,0)

t.color("pink")

angle=0

move_to(-100,-200)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-100,-200)

t.color("lightgreen")

angle=0

move_to(300,0)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(300,0)

t.color("skyblue")

angle=0

move_to(-500,-240)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-500,-240)

t.color("orange")

angle=0

move_to(-350,-170)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-350,-170)

t.color("black")

angle=0

move_to(500,20)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(500,20)

    #clcoding.com

Python Coding challenge - Day 66 | What is the output of the following Python code?

 


In Python, when you multiply a boolean value by an integer, the boolean value is implicitly converted to an integer. In this case, False is equivalent to 0, so False * 10 will result in 0.

If you run the following code: print(False * 10)

The output will be: 0

Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python (Free Course)

 


What you'll learn

How to solve a partial differential equation using the finite-difference, the pseudospectral, or the linear (spectral) finite-element method.

Understanding the limits of explicit space-time simulations due to the stability criterion and spatial and temporal sampling requirements.

Strategies how to plan and setup sophisticated simulation tasks.

Strategies how to avoid errors in simulation results. 

There are 9 modules in this course

Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models. 

The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences,  engineering, as well as economics and other fields. 

Join - Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

20 extremely useful single-line Python codes

 





#!/usr/bin/env python

# coding: utf-8


# # 20 extremely useful single-line Python codes


# #  1.  Swap Variables:


# In[ ]:



a, b = b, a



# # 2. Find Maximum Element in a List:


# In[ ]:



max_element = max(lst)



# # 3. Find Minimum Element in a List:


# In[ ]:



min_element = min(lst)



# # 4. List Comprehension:


# In[ ]:



squared_numbers = [x**2 for x in range(10)]



# # 5. Filter List Elements:


# In[ ]:



even_numbers = list(filter(lambda x: x % 2 == 0, lst))



# # 6. Map Function:


# In[ ]:



doubled_numbers = list(map(lambda x: x * 2, lst))



# # 7. Sum of List Elements:


# In[ ]:



total = sum(lst)



# # 8. Check if All Elements in a List are True:


# In[ ]:



all_true = all(lst)



# # 9. Check if Any Element in a List is True:


# In[ ]:



any_true = any(lst)



# # 10. Count Occurrences of an Element in a List:


# In[ ]:



count = lst.count(element)



# # 11. Reverse a String:


# In[ ]:



reversed_str = my_str[::-1]



# # 12. Read a File into a List of Lines:


# In[ ]:



lines = [line.strip() for line in open('file.txt')]



# # 13. Inline If-Else:


# In[ ]:



result = "even" if x % 2 == 0 else "odd"



# # 14. Flatten a Nested List:


# In[ ]:



flat_list = [item for sublist in nested_list for item in sublist]



# # 15. Find the Factorial of a Number:


# In[ ]:



factorial = 1 if n == 0 else functools.reduce(lambda x, y: x * y, range(1, n+1))



# # 16. List Unique Elements:


# In[ ]:



unique_elements = list(set(lst))



# # 17. Execute a Function for Each Element in a List:


# In[ ]:



result = list(map(lambda x: my_function(x), lst))



# # 18. Calculate the Average of a List:


# In[ ]:



average = sum(lst) / len(lst) if len(lst) > 0 else 0



# # 19. Convert a String to a List of Characters:


# In[ ]:



char_list = list("hello")



# # 20. Find Common Elements Between Two Lists:


# In[ ]:



common_elements = list(set(lst1) & set(lst2))



# In[ ]:


Friday 10 November 2023

Get Started with Stacks and Queues in Python

 


Stacks and queues are fundamental data structures used in computer science to manage and organize data. Let's get started with stacks and queues in Python.

Stacks:

A stack is a Last In, First Out (LIFO) data structure, where the last element added is the first one to be removed. Think of it like a stack of plates - you can only take the top plate off.

Implementation in Python:

class Stack:
    def __init__(self):
        self.items = []

    def is_empty(self):
        return len(self.items) == 0

    def push(self, item):
        self.items.append(item)

    def pop(self):
        if not self.is_empty():
            return self.items.pop()
        else:
            raise IndexError("pop from an empty stack")

    def peek(self):
        if not self.is_empty():
            return self.items[-1]
        else:
            raise IndexError("peek from an empty stack")

    def size(self):
        return len(self.items)

Example usage:

stack = Stack()
stack.push(1)
stack.push(2)
stack.push(3)

print("Stack:", stack.items)

print("Pop:", stack.pop())
print("Stack after pop:", stack.items)

print("Peek:", stack.peek())
print("Stack size:", stack.size())

Queues:

A queue is a First In, First Out (FIFO) data structure, where the first element added is the first one to be removed. Think of it like a queue of people waiting in line.

Implementation in Python:

class Queue:
    def __init__(self):
        self.items = []

    def is_empty(self):
        return len(self.items) == 0

    def enqueue(self, item):
        self.items.append(item)

    def dequeue(self):
        if not self.is_empty():
            return self.items.pop(0)
        else:
            raise IndexError("dequeue from an empty queue")

    def front(self):
        if not self.is_empty():
            return self.items[0]
        else:
            raise IndexError("front from an empty queue")

    def size(self):
        return len(self.items)


Example usage:

queue = Queue()
queue.enqueue(1)
queue.enqueue(2)
queue.enqueue(3)

print("Queue:", queue.items)

print("Dequeue:", queue.dequeue())
print("Queue after dequeue:", queue.items)

print("Front:", queue.front())
print("Queue size:", queue.size())

These basic implementations should get you started with stacks and queues in Python. Feel free to modify and expand upon them based on your specific needs.

Programming for Everybody (Getting Started with Python)

 


What you'll learn

Install Python and write your first program

Describe the basics of the Python programming language

Use variables to store, retrieve and calculate information

Utilize core programming tools such as functions and loops

There are 7 modules in this course

This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python.  The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”.  Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.

Join - Programming for Everybody (Getting Started with Python)

Python Coding challenge - Day 65 | What is the output of the following Python code?

 


Code - 

def add(a, b):

    return a + 5 , b + 5

print(add(10,11))

Solution - 

The add function takes two parameters, a and b, and returns a tuple where the first element is the sum of a + 5 and the second element is b + 5.

When you call add(10, 11), it will return a tuple where the first element is 10 + 5 (which is 15) and the second element is 11 + 5 (which is 16). Therefore, the output of print(add(10, 11)) will be: (15, 16)



Thursday 9 November 2023

Python Coding challenge - Day 64 | What is the output of the following Python code?

 

Code- 

s = set()
s.update('hello', 'how', 'are', 'you?')
print(len(s))

Solution - 

The above code counts the total number of unique characters in the given strings. Here's the breakdown:

Create an empty set s:

s = set()

This line initializes an empty set s.

Update the set with multiple string arguments using the update method:

s.update('hello', 'how', 'are', 'you?')

In this line, you're using the update method to add the characters from the strings 'hello', 'how', 'are', and 'you?' to the set s.

Print the length of the set:

print(len(s))

This line prints the length (number of elements) of the set s using the len function. Since each character is considered unique, the length will be the total number of unique characters in the combined strings.

The output will be 10 because it counts the total number of unique characters in the provided strings. Thank you for pointing this out.


Wednesday 8 November 2023

Mastering Python for Artificial Intelligence: Learn the Essential Coding Skills to Build Advanced AI Applications (Free PDF)


 

Are you fascinated by the possibilities of Artificial Intelligence but feel limited by your current coding skills? Do you dream of creating advanced AI applications but need help finding the right resources?

Look no further! "Mastering Python for Artificial Intelligence" is your gateway to learning the essential coding skills that will empower you to build cutting-edge AI applications.

Whether you're a beginner or an experienced programmer, this book will guide you through Python's intricacies and equip you with the knowledge to unleash the true potential of AI.


Mastering Python for Artificial Intelligence" offers an innovative approach encompassing three well-defined principles, ensuring an empowering learning journey for readers.

1. Practicality: The book strongly believes in the value of learning by doing. Unlike many other resources, "Mastering Python for Artificial Intelligence" immediately provides the outputs of ALL the examples. Readers won't have to wait to test the code on their computers or wonder if they are on the right track. This practical approach ensures hands-on experience, reinforcing knowledge and boosting confidence.

2. Simplicity: Learning complex subjects should be approached step by step, and "Mastering Python for Artificial Intelligence" embraces this principle. Each concept is broken down into simple and easily digestible steps. The book aims to make learning efficient and enjoyable, allowing readers to grasp a multitude of topics in the shortest possible time. Clear explanations and examples accompany the content, ensuring rapid progress and understanding.

3. Synthesis: Recognizing that starting with Python can be overwhelming, this book takes a thoughtful approach. Carefully selected topics provide a comprehensive introduction to Python, offering a solid foundation without overwhelming the reader. By presenting essential concepts in a structured manner, the book ensures broad exposure to Python and its applications in Artificial Intelligence.


Here's a sneak peek into what you'll discover:

• Gain a solid understanding of Python's notable features and why it is the preferred language for AI development.

• Learn the step-by-step process of Python IDE installation, ensuring you have the optimal environment for AI programming.

• Explore Python programming fundamentals, including variables, statements, operators, and flow control, laying the groundwork for AI development.

• Dive into the world of data types, such as numeric, sequence, string, list, tuple, set, and dictionary, and understand how they play a crucial role in AI applications.

• Unleash the potential of Python classes and objects and understand how they form the building blocks of AI models and algorithms.

• Discover the wealth of Python libraries and frameworks available for AI development, such as TensorFlow, Keras, scikit-learn, and more.

• Learn how to preprocess data, train AI models, and evaluate their performance using Python's powerful AI libraries.

• Get hands-on experience with practical coding examples and exercises, allowing you to apply your newfound knowledge and solidify your skills.

• The SOLUTIONS to the exercises (but be sure to look at them only after first trying to solve the exercises on your own)

• BONUS: EMPOWERING YOUR LIFE: Harnessing the Power of Chat GPT and Python to Create Your Personal Assistant (scan the QR code inside the book)

Buy - Mastering Python for Artificial Intelligence: Learn the Essential Coding Skills to Build Advanced AI Applications

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