def astar():
    s="""import heapq

def astar(start, goal, heuristic_func, neighbors_func):

    frontier = [(0, start)]
    came_from = {}
    cost_so_far = {start: 0}

    while frontier:
        _, current = heapq.heappop(frontier)

        if current == goal:
            break

        for neighbor in neighbors_func(current):
            new_cost = cost_so_far[current] + 1  # assume edge cost of 1
            if neighbor not in cost_so_far or new_cost < cost_so_far[neighbor]:
                cost_so_far[neighbor] = new_cost
                priority = new_cost + heuristic_func(neighbor, goal)
                heapq.heappush(frontier, (priority, neighbor))
                came_from[neighbor] = current

    path = []
    total_cost = 0
    if current == goal:
        while current != start:
            path.append(current)
            current = came_from[current]
        path.append(start)
        path.reverse()
        total_cost = cost_so_far[goal]

    return path, total_cost
    start = (0, 0)
goal = (4, 4)

def euclidean_distance(a, b):
    x1, y1 = a
    x2, y2 = b
    return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5

def neighbors(node):
    x, y = node
    return [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]

path, cost = astar(start, goal, euclidean_distance, neighbors)

print("Path:", path)
print("Cost:", cost)


    """
    return s
def puzzle():
    s="""import copy
from heapq import heappush, heappop
row = [ 1, 0, -1, 0 ]
col = [ 0, -1, 0, 1 ]
class priorityQueue:
	def __init__(self):
		self.heap = []
	def push(self, k):
		heappush(self.heap, k)
	def pop(self):
		return heappop(self.heap)
	def empty(self):
		if not self.heap:
			return True
		else:
			return False

# Node structure
class node:

	def __init__(self, parent, mat, empty_tile_pos,
				cost, level):
		self.parent = parent
		self.mat = mat
		self.empty_tile_pos = empty_tile_pos
		self.cost = cost
		self.level = level
	def __lt__(self, nxt):
		return self.cost < nxt.cost
def calculateCost(mat, final) -> int:

	count = 0
	for i in range(n):
		for j in range(n):
			if ((mat[i][j]) and
				(mat[i][j] != final[i][j])):
				count += 1

	return count

def newNode(mat, empty_tile_pos, new_empty_tile_pos,
			level, parent, final) -> node:
	new_mat = copy.deepcopy(mat)
	x1 = empty_tile_pos[0]
	y1 = empty_tile_pos[1]
	x2 = new_empty_tile_pos[0]
	y2 = new_empty_tile_pos[1]
	new_mat[x1][y1], new_mat[x2][y2] = new_mat[x2][y2], new_mat[x1][y1]
	cost = calculateCost(new_mat, final)

	new_node = node(parent, new_mat, new_empty_tile_pos,
					cost, level)
	return new_node
def printMatrix(mat):

	for i in range(n):
		for j in range(n):
			print("%d " % (mat[i][j]), end = " ")
		print()
def isSafe(x, y):
	return x >= 0 and x < n and y >= 0 and y < n
def printPath(root):

	if root == None:
		return

	printPath(root.parent)
	printMatrix(root.mat)
	print()
def solve(initial, empty_tile_pos, final):
	pq = priorityQueue()
	cost = calculateCost(initial, final)
	root = node(None, initial,
				empty_tile_pos, cost, 0)
	pq.push(root)
	while not pq.empty():
		minimum = pq.pop()
		if minimum.cost == 0:
			printPath(minimum)
			return
		for i in range(4):
			new_tile_pos = [
				minimum.empty_tile_pos[0] + row[i],
				minimum.empty_tile_pos[1] + col[i], ]

			if isSafe(new_tile_pos[0], new_tile_pos[1]):
				child = newNode(minimum.mat,
								minimum.empty_tile_pos,
								new_tile_pos,
								minimum.level + 1,
								minimum, final,)
				pq.push(child)
initial = [ [ 1, 2, 3 ],
			[ 5, 6, 0 ],
			[ 7, 8, 4 ] ]
final = [ [ 1, 2, 3 ],
		[ 5, 8, 6 ],
		[ 0, 7, 4 ] ]
empty_tile_pos = [ 1, 2 ]
solve(initial, empty_tile_pos, final)
"""
    return s

def agentproblem():
    s="""import time
import random
class TrafficLight:
    def __init__(self):
        self.__color = "red"

    def change_color(self):
        if self.__color == "red":
            self.__color = "green"
        elif self.__color == "green":
            self.__color = "yellow"
        elif self.__color == "yellow":
            self.__color = "red"

    def get_color(self):
        return self.__color

    def get_wait_time(self):
        if self.__color == "red":
            return 5 + random.randint(0, 5)
        elif self.__color == "green":
            return 10 + random.randint(0, 10)
        elif self.__color == "yellow":
            return 2 + random.randint(0, 2)

if __name__ == '__main__':
    light = TrafficLight()
    while True:
        print("Light is", light.get_color())
        wait_time = light.get_wait_time()
        print("Waiting for", wait_time, "seconds")
        time.sleep(wait_time)
        light.change_color() """
    return s

def bfs_and_dfs():
    s="""graph = {
  'A' : ['B','C'],
  'B' : ['D', 'E'],
  'C' : ['F'],
  'D' : [],
  'E' : ['F'],
  'F' : []
}

visited_bfs = []
queue = []

def bfs(visited_bfs, graph, node):
  visited_bfs.append(node)
  queue.append(node)

  while queue:
    s = queue.pop(0)
    print (s, end = " ")

    for neighbour in graph[s]:
      if neighbour not in visited_bfs:
        visited_bfs.append(neighbour)
        queue.append(neighbour)

visited = set()

def dfs(visited, graph, node):
    if node not in visited:
        print (node, end=" ")
        visited.add(node)
        for neighbour in graph[node]:
            dfs(visited, graph, neighbour)

print("BFS:" , end =" ")
bfs(visited_bfs, graph, 'A')
print('
')
print("DFS:" , end =" ")
dfs(visited, graph, 'A')"""
    return s

def best_first_search():
    s="""from queue import PriorityQueue
v = 14
graph = [[] for i in range(v)]

# Function For Implementing Best First Search
# Gives output path having lowest cost


def best_first_search(actual_Src, target, n):
    visited = [False] * n
    pq = PriorityQueue()
    pq.put((0, actual_Src))
    visited[actual_Src] = True

    while pq.empty() == False:
        u = pq.get()[1]
        # Displaying the path having lowest cost
        print(u, end=" ")
        if u == target:
            break

        for v, c in graph[u]:
            if visited[v] == False:
                visited[v] = True
                pq.put((c, v))
    print()

# Function for adding edges to graph


def addedge(x, y, cost):
    graph[x].append((y, cost))
    graph[y].append((x, cost))


# The nodes shown in above example(by alphabets) are
# implemented using integers addedge(x,y,cost);
addedge(0, 1, 3)
addedge(0, 2, 6)
addedge(0, 3, 5)
addedge(1, 4, 9)
addedge(1, 5, 8)
addedge(2, 6, 12)
addedge(2, 7, 14)
addedge(3, 8, 7)
addedge(8, 9, 5)
addedge(8, 10, 6)
addedge(9, 11, 1)
addedge(9, 12, 10)
addedge(9, 13, 2)

source = 0
target = 9
best_first_search(source, target, v)"""
    return s

def graph_color():
    s="""from constraint import *


problem = Problem()


problem.addVariables(['A', 'B', 'C', 'D', 'E'], ['red', 'green', 'blue'])

problem.addConstraint(lambda a, b: a != b, ['A', 'B'])
problem.addConstraint(lambda a, b: a != b, ['A', 'C'])
problem.addConstraint(lambda a, b: a != b, ['B', 'C'])
problem.addConstraint(lambda a, b: a != b, ['B', 'D'])
problem.addConstraint(lambda a, b: a != b, ['C', 'D'])
problem.addConstraint(lambda a, b: a != b, ['C', 'E'])
problem.addConstraint(lambda a, b: a != b, ['D', 'E'])


solutions = problem.getSolutions()


for solution in solutions:
    print(solution)"""
    return s

def minimax():
    s="""
MAX, MIN = 1000, -1000
def minimax(depth, nodeIndex, maximizingPlayer,values, alpha, beta):
    if depth == 3:
        return values[nodeIndex]

    if maximizingPlayer:
        best = MIN

        for i in range(0, 2):
            val = minimax(depth + 1, nodeIndex * 2 + i,False, values, alpha, beta)
            best = max(best, val)
            alpha = max(alpha, best)

            if beta <= alpha:
                break
        return best

    else:
        best = MAX
        for i in range(0, 2):
            val = minimax(depth + 1, nodeIndex * 2 + i,True, values, alpha, beta)
            best = min(best, val)
            beta = min(beta, best)

            if beta <= alpha:
                break
        return best

if __name__ == "__main__":
    values = [3, 5, 6, 9, 1, 2, 0, -1]
    print("The optimal value is :", minimax(0, 0, True, values, MIN, MAX))

"""
    return s

def propsitional_logic():
    s="""p = True
q = False

not_p = not p
and_pq = p and q
or_pq = p or q
implication_pq = (not p) or q
biconditional_pq = (p and q) or (not p and not q)

print("not p:", not_p)
print("p and q:", and_pq)
print("p or q:", or_pq)
print("p implies q:", implication_pq)
print("p if and only if q:", biconditional_pq)
r= True
s= False
res= not ((p and q) or (not r))  or s
print("result is: ",res)"""
    return s

def unification():
    s="""def unify(x, y, substitutions):
    # condition 1- Check if x and y are already equal
    if x == y:
        return substitutions

    # If x or y is a variable, substitute it with the other term
    if is_variable(x):
        return substitute(x, y, substitutions)
    elif is_variable(y):
        return substitute(y, x, substitutions)

    # If x and y are lists, unify their elements recursively
    if is_list(x) and is_list(y):
        if len(x) != len(y):
            return False
        else:
            for i in range(len(x)):
                substitutions = unify(x[i], y[i], substitutions)
                if substitutions is False:
                    return False
            return substitutions

    # If x and y are constants, they cannot be unified
    return False
x = ['loves', 'John', 'Mary']
y = ['loves', 'x', 'y']
substitutions = unify(x, y, {})
print(substitutions)"""
    return s

def knowledge():
    s="""
lion = {
    "name": "Lion",
    "type": "Mammal",
    "habitat": "Grasslands",
    "diet": "Carnivorous"
}


monkey = {
    "name": "Monkey",
    "type": "Mammal",
    "habitat": "Forest",
    "diet": "Omnivorous"
}


penguin = {
    "name": "Penguin",
    "type": "Bird",
    "habitat": "Antarctica",
    "diet": "Carnivorous"
}
print("Name:", lion["name"])
print("Habitat:", lion["habitat"])
# Define a frame for a cat that inherits from the mammal frame
cat = {
    "name": "Cat",
    "superclass": monkey,
    "habitat": "Domestic",
    "diet": "Carnivorous",
    "has_fur": True,
    "can_climb": True
}
print("Type:", cat["superclass"]["type"])"""
    return s

def bayesian():
    s="""# Importing modules that are required
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.linear_model import BayesianRidge

# Loading dataset
dataset = load_boston()
X, y = dataset.data, dataset.target

# Splitting dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state = 42)

# Creating and training model
model = BayesianRidge()
model.fit(X_train, y_train)

# Model making a prediction on test data
prediction = model.predict(X_test)

# Evaluation of r2 score of the model against the test set
print(f"r2 Score Of Test Set : {r2_score(y_test, prediction)}")"""
    return s

def resolution():
    s="""from typing import Dict, Union

# Unification algorithm
def unify(x: Union[str, int], y: Union[str, int], theta: Dict) -> Union[None, Dict]:
    if theta is None:
        return None
    elif x == y:
        return theta
    elif isinstance(x, str):
        return unify_var(x, y, theta)
    elif isinstance(y, str):
        return unify_var(y, x, theta)
    elif isinstance(x, list) and isinstance(y, list):
        return unify(x[1:], y[1:], unify(x[0], y[0], theta))
    else:
        return None

def unify_var(var: str, x: Union[str, int], theta: Dict) -> Union[None, Dict]:
    if var in theta:
        return unify(theta[var], x, theta)
    elif x in theta:
        return unify(var, theta[x], theta)
    else:
        theta[var] = x
        return theta

# Resolution algorithm
def resolution(c1: str, c2: str) -> Union[str, None]:
    clauses = [c1, c2]
    new_clauses = set()
    while True:
        n = len(clauses)
        for i in range(n):
            for j in range(i+1, n):
                resolvents = resolve(clauses[i], clauses[j])
                if resolvents is not None:
                    for resolvent in resolvents:
                        if resolvent == "":
                            return ""
                        new_clauses.add(resolvent)
        if new_clauses.issubset(clauses):
            return None
        for clause in new_clauses:
            if clause not in clauses:
                clauses.append(clause)

def resolve(c1: str, c2: str) -> Union[str, None]:
    for l1 in c1.split(" OR "):
        for l2 in c2.split(" OR "):
            theta = unify(l1, negate(l2), {})
            if theta is not None:
                resolvent = substitute(c1 + " OR " + c2, theta)
                if resolvent != c1 and resolvent != c2:
                    return resolvent
    return None

def negate(literal: str) -> str:
    if literal.startswith("~"):
        return literal[1:]
    else:
        return "~" + literal

def substitute(clause: str, theta: Dict) -> str:
    literals = [substitute_literal(literal, theta) for literal in clause.split(" OR ")]
    return " OR ".join(literals)

def substitute_literal(literal: str, theta: Dict) -> str:
    for var, val in theta.items():
        literal = literal.replace(var, str(val))
    return literal
# Test unification
print(unify("x", "John", {})) # {"x": "John"}
print(unify("John", "John", {})) # {}
print(unify("x", "y", {"x": "John"})) # {"x": "John", "y": "John"}
print(unify("father(x, y)", "father(John, Bill)", {})) # {"x": "John", "y": "Bill"}
print(unify("f(g(x, a), y)", "f(g(b, z), c)", {})) # {"x": "b", "y": "c", "z": "a"}

# Test resolution
print(resolution("p(x) OR q(x)", "~q(a) OR r(b) OR p(y)")) # "p(y) OR r(b)"
print(resolution("p(x) OR q(x)", "~q(a) OR r(b) OR s(y)")) # None
"""
    return s

def vaccum():
    s="""import random

def display(room):
    print(room)

room = [
    [1, 1, 1, 1],
    [1, 1, 1, 1],
    [1, 1, 1, 1],
    [1, 1, 1, 1],
]
print("All the rooom are dirty")
display(room)

x =0
y= 0

while x < 4:
    while y < 4:
        room[x][y] = random.choice([0,1])
        y+=1
    x+=1
    y=0

print("Before cleaning the room I detect all of these random dirts")
display(room)
x =0
y= 0
z=0
while x < 4:
    while y < 4:
        if room[x][y] == 1:
            print("Vaccum in this location now,",x, y)
            room[x][y] = 0
            print("cleaned", x, y)
            z+=1
        y+=1
    x+=1
    y=0
pro= (100-((z/16)*100))
print("Room is clean now, Thanks for using : cleaner")
display(room)
print('performance=',pro,'%')"""
    return s