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question:from typing import List, Tuple def can_transform(s: str, t: str) -> bool: Determines if string s can be transformed to string t by reversing segments of s. >>> can_transform("abac", "baca") True >>> can_transform("hello", "holle") True >>> can_transform("abcde", "edcba") True >>> can_transform("abc", "def") False def process_test_cases(test_cases: List[Tuple[str, str]]) -> List[str]: Processes multiple test cases and returns the results for each case. >>> test_cases = [("abac", "baca"), ("hello", "holle"), ("abcde", "edcba")] >>> process_test_cases(test_cases) ["YES", "YES", "YES"] >>> test_cases = [("abc", "def"), ("abcd", "abc")] >>> process_test_cases(test_cases) ["NO", "NO"]
answer:def can_transform(s, t): Determines if string s can be transformed to string t by reversing segments of s. return sorted(s) == sorted(t) def process_test_cases(test_cases): Processes multiple test cases and returns the results for each case. results = [] for s, t in test_cases: if can_transform(s, t): results.append("YES") else: results.append("NO") return results
question:def maximize_area(l: int) -> (int, int): Given the length of the fencing material, return the width and height of the rectangle that maximizes the area and can be fully enclosed by the fencing. Examples: >>> maximize_area(14) in [(3, 4), (4, 3)] >>> maximize_area(20) in [(5, 5)] >>> w, h = maximize_area(1000000) >>> w + h == 1000000 // 2 >>> w * h == (1000000 // 4) ** 2 >>> maximize_area(4) in [(1, 1)] >>> w, h = maximize_area(999999) >>> w + h == 999999 // 2
answer:def maximize_area(l): Given the length of the fencing material, this function returns the width and height of the rectangle that maximizes the area and can be fully enclosed by the fencing. max_area = 0 best_w = 1 best_h = l // 2 - 1 for w in range(1, l // 2): h = l // 2 - w area = w * h if area > max_area: max_area = area best_w = w best_h = h return best_w, best_h
question:def solution(T, cases): Determines whether it is possible to complete the project given the availability of the members. If it is possible, the function outputs all possible sets of intervals for each member that sum to the task requirement (8 hours). If it is not possible, output "Not possible". Args: T: int, number of test cases. cases: list of lists, where each list contains the number of members followed by the number of hours each member has available. Returns: List of strings with the result for each test case. Test Cases: >>> solution(2, [[4, 1, 2, 3, 2], [3, 5, 4, 7]]) ["Case #1: Possible", "Case #2: Not possible"] >>> solution(1, [[3, 3, 3, 3]]) ["Case #1: Not possible"] >>> solution(1, [[2, 8, 8]]) ["Case #1: Possible"] >>> solution(1, [[4, 1, 7, 3, 5]]) ["Case #1: Possible"] >>> solution(1, [[3, 1, 2, 3]]) ["Case #1: Not possible"]
answer:def can_form_eight_hours(hours): n = len(hours) dp = [False] * 25 # We care up to sum 24 (maximum hour given by constraint) dp[0] = True for h in hours: for j in range(24, h-1, -1): dp[j] = dp[j] or dp[j-h] return dp[8] def solve(test_cases): results = [] case_number = 1 for hours in test_cases: if can_form_eight_hours(hours): results.append(f"Case #{case_number}: Possible") else: results.append(f"Case #{case_number}: Not possible") case_number += 1 return results def solution(T, cases): test_cases = [] for case in cases: N, *hours = case test_cases.append(hours) return solve(test_cases) # Example input and output # (normally, you would handle input from stdin and output to stdout) T = 2 cases = [ [4, 1, 2, 3, 2], [3, 5, 4, 7] ] output = solution(T, cases) for out in output: print(out)
question:def harvest_order(t: int, test_cases: List[Tuple[int, List[Tuple[int, int]]]]) -> List[List[Tuple[int, int]]]: Identify the order in which the fruits should be harvested to maximize the magical power they absorb from the environment. >>> harvest_order(2, [(3, [(1, 2), (3, 1), (2, 2)]), (4, [(-1, -1), (2, 2), (-2, 2), (1, -1)])]) [[(1, 2), (2, 2), (3, 1)], [(-1, -1), (1, -1), (2, 2), (-2, 2)]] >>> harvest_order(1, [(5, [(0, 1), (1, 0), (1, 1), (-1, -1), (0, -1)])]) [[(0, 1), (1, 1), (1, 0), (0, -1), (-1, -1)]]
answer:import math def euclidean_distance(p1, p2): return math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) def harvest_order(t, test_cases): results = [] for i in range(t): n, fruits = test_cases[i] if n == 0: results.append([]) continue current_position = (0, 0) unvisited_fruits = fruits.copy() order_of_harvest = [] while unvisited_fruits: closest_fruit = min(unvisited_fruits, key=lambda fruit: euclidean_distance(current_position, fruit)) order_of_harvest.append(closest_fruit) unvisited_fruits.remove(closest_fruit) current_position = closest_fruit results.append(order_of_harvest) return results