P

V

N

M

K

N

p

N

```
from collections.abc import Callable
def levenshtein_distance(first_word: str, second_word: str) -> int:
"""
Implementation of the Levenshtein distance in Python.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the levenshtein distance between the two words.
Examples:
>>> levenshtein_distance("planet", "planetary")
3
>>> levenshtein_distance("", "test")
4
>>> levenshtein_distance("book", "back")
2
>>> levenshtein_distance("book", "book")
0
>>> levenshtein_distance("test", "")
4
>>> levenshtein_distance("", "")
0
>>> levenshtein_distance("orchestration", "container")
10
"""
# The longer word should come first
if len(first_word) < len(second_word):
return levenshtein_distance(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1]
for j, c2 in enumerate(second_word):
# Calculate insertions, deletions, and substitutions
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
# Get the minimum to append to the current row
current_row.append(min(insertions, deletions, substitutions))
# Store the previous row
previous_row = current_row
# Returns the last element (distance)
return previous_row[-1]
def levenshtein_distance_optimized(first_word: str, second_word: str) -> int:
"""
Compute the Levenshtein distance between two words (strings).
The function is optimized for efficiency by modifying rows in place.
:param first_word: the first word to measure the difference.
:param second_word: the second word to measure the difference.
:return: the Levenshtein distance between the two words.
Examples:
>>> levenshtein_distance_optimized("planet", "planetary")
3
>>> levenshtein_distance_optimized("", "test")
4
>>> levenshtein_distance_optimized("book", "back")
2
>>> levenshtein_distance_optimized("book", "book")
0
>>> levenshtein_distance_optimized("test", "")
4
>>> levenshtein_distance_optimized("", "")
0
>>> levenshtein_distance_optimized("orchestration", "container")
10
"""
if len(first_word) < len(second_word):
return levenshtein_distance_optimized(second_word, first_word)
if len(second_word) == 0:
return len(first_word)
previous_row = list(range(len(second_word) + 1))
for i, c1 in enumerate(first_word):
current_row = [i + 1] + [0] * len(second_word)
for j, c2 in enumerate(second_word):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row[j + 1] = min(insertions, deletions, substitutions)
previous_row = current_row
return previous_row[-1]
def benchmark_levenshtein_distance(func: Callable) -> None:
"""
Benchmark the Levenshtein distance function.
:param str: The name of the function being benchmarked.
:param func: The function to be benchmarked.
"""
from timeit import timeit
stmt = f"{func.__name__}('sitting', 'kitten')"
setup = f"from __main__ import {func.__name__}"
number = 25_000
result = timeit(stmt=stmt, setup=setup, number=number)
print(f"{func.__name__:<30} finished {number:,} runs in {result:.5f} seconds")
if __name__ == "__main__":
# Get user input for words
first_word = input("Enter the first word for Levenshtein distance:\n").strip()
second_word = input("Enter the second word for Levenshtein distance:\n").strip()
# Calculate and print Levenshtein distances
print(f"{levenshtein_distance(first_word, second_word) = }")
print(f"{levenshtein_distance_optimized(first_word, second_word) = }")
# Benchmark the Levenshtein distance functions
benchmark_levenshtein_distance(levenshtein_distance)
benchmark_levenshtein_distance(levenshtein_distance_optimized)
```