From 94e7bf7d46343751d44372ea3dd20c0c8c2da773 Mon Sep 17 00:00:00 2001 From: Beck LaBash <55890162+becklabs@users.noreply.github.com> Date: Sun, 16 Apr 2023 20:55:36 -0400 Subject: [PATCH] Prompts --- 2023-04-16_12-09-25 | 4 + executors/leet_executor.py | 6 +- executors/py_executor.py | 8 +- generators/generator_utils.py | 59 +++-- generators/py_generate.py | 229 +++++++++++++++++- reflexion.py | 2 +- ...ed._reflexion_5_gpt-4_pass_at_k_1_py.jsonl | 2 + ...ts._reflexion_5_gpt-4_pass_at_k_1_py.jsonl | 34 +++ ...ts._reflexion_5_gpt-4_pass_at_k_1_py.jsonl | 8 + ...ts._reflexion_5_gpt-4_pass_at_k_1_py.jsonl | 6 + run_reflexion_py_leet.sh | 4 +- 11 files changed, 315 insertions(+), 47 deletions(-) create mode 100644 2023-04-16_12-09-25 create mode 100644 root/reflexion_leetcode_python3_gpt4_react/leetcode-hard-py-40-uncontaminated._reflexion_5_gpt-4_pass_at_k_1_py.jsonl create mode 100644 root/reflexion_leetcode_python3_gpt4_react_constraints/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl create mode 100644 root/reflexion_leetcode_python3_gpt4_react_constraints_3_tests/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl create mode 100644 root/reflexion_leetcode_python3_gpt4_react_constraints_test/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl diff --git a/2023-04-16_12-09-25 b/2023-04-16_12-09-25 new file mode 100644 index 0000000..cc19c04 --- /dev/null +++ b/2023-04-16_12-09-25 @@ -0,0 +1,4 @@ +{"name": "split-message-based-on-limit", "status": "Wrong Answer", "reward": false, "info": {"status_code": 11, "lang": "python3", "run_success": true, "status_runtime": "N/A", "memory": 14972000, "question_id": "2563", "elapsed_time": 148, "compare_result": "0111111111111011000011000000000000000000000000000011100000000000000000000000000111111101111000", "code_output": "[\"this<1/10>\",\" is <2/10>\",\"real<3/10>\",\"ly a<4/10>\",\" ver<5/10>\",\"y aw<6/10>\",\"esom<7/10>\",\"e me<8/10>\",\"ssag<9/10>\",\"e<10/10>\"]", "std_output": "", "last_testcase": "\"this is really a very awesome message\"\n9", "expected_output": "[\"thi<1/14>\",\"s i<2/14>\",\"s r<3/14>\",\"eal<4/14>\",\"ly <5/14>\",\"a v<6/14>\",\"ery<7/14>\",\" aw<8/14>\",\"eso<9/14>\",\"me<10/14>\",\" m<11/14>\",\"es<12/14>\",\"sa<13/14>\",\"ge<14/14>\"]", "task_finish_time": 1681661576324, "total_correct": 30, "total_testcases": 94, "runtime_percentile": null, "status_memory": "N/A", "memory_percentile": null, "pretty_lang": "Python3", "submission_id": "934796442", "input_formatted": "\"this is really a very awesome message\", 9", "input": "\"this is really a very awesome message\"\n9", "status_msg": "Wrong Answer", "state": "SUCCESS"}} +{"name": "maximum-number-of-non-overlapping-palindrome-substrings", "status": "Submission Timed-Out", "reward": false, "info": {"state": "STARTED"}} +{"name": "minimum-total-distance-traveled", "status": "Accepted", "reward": true, "info": {"status_code": 10, "lang": "python3", "run_success": true, "status_runtime": "4528 ms", "memory": 94288000, "question_id": "2554", "elapsed_time": 4649, "compare_result": "1111111111111111111111111111111111111111", "code_output": "", "std_output": "", "last_testcase": "", "expected_output": "", "task_finish_time": 1681662197849, "total_correct": 40, "total_testcases": 40, "runtime_percentile": 5.600000000000221, "status_memory": "94.3 MB", "memory_percentile": 49.60000000000006, "pretty_lang": "Python3", "submission_id": "934801257", "status_msg": "Accepted", "state": "SUCCESS"}} +{"name": "next-greater-element-iv", "status": "Wrong Answer", "reward": false, "info": {"status_code": 11, "lang": "python3", "run_success": true, "status_runtime": "N/A", "memory": 27900000, "question_id": "2549", "elapsed_time": 1120, "compare_result": "010000001000000000000000000000000000000000010100000", "code_output": "[4,9,9,-1,-1]", "std_output": "", "last_testcase": "[2,4,0,9,6]", "expected_output": "[9,6,6,-1,-1]", "task_finish_time": 1681662380199, "total_correct": 4, "total_testcases": 51, "runtime_percentile": null, "status_memory": "N/A", "memory_percentile": null, "pretty_lang": "Python3", "submission_id": "934802716", "input_formatted": "[2,4,0,9,6]", "input": "[2,4,0,9,6]", "status_msg": "Wrong Answer", "state": "SUCCESS"}} diff --git a/executors/leet_executor.py b/executors/leet_executor.py index 7f1e62a..52733e7 100644 --- a/executors/leet_executor.py +++ b/executors/leet_executor.py @@ -24,7 +24,11 @@ class LeetExecutor(Executor): from .leetcode_env.leetcode_env.leetcode_types import LeetCodeSubmission from .leetcode_env.leetcode_env.utils import id_from_slug print(f'Timeout is {timeout} seconds') - leetcode_formatted_func = self.formatter.to_leetcode(func) + try: + leetcode_formatted_func = self.formatter.to_leetcode(func) + except Exception as e: + print(f'Error formatting function to leetcode: {e}') + return False print('----------------- LEETCODE SUBMISSION ------------------') print(leetcode_formatted_func) print('--------------------------------------------------------') diff --git a/executors/py_executor.py b/executors/py_executor.py index cef2e95..301da02 100644 --- a/executors/py_executor.py +++ b/executors/py_executor.py @@ -2,7 +2,7 @@ import ast import signal import astunparse -from .executor_utils import timeout_handler, function_with_timeout +from .executor_utils import function_with_timeout from typing import List from .executor_types import ExecuteResult, Executor @@ -78,11 +78,9 @@ def get_call_str(assert_statement: str) -> str: def get_output(func: str, assert_statement: str, timeout: int = 5) -> str: try: + exec(f"from typing import *\n{func}", globals()) func_call = get_call_str(assert_statement) - to_eval = f"from typing import *\n{func}\n{func_call}" - exec(func, globals()) - output = function_with_timeout(eval, (func_call,globals()), timeout) - + output = function_with_timeout(eval, (func_call, globals()), timeout) return output except TimeoutError: return "TIMEOUT" diff --git a/generators/generator_utils.py b/generators/generator_utils.py index 4a5e761..3fa15a1 100644 --- a/generators/generator_utils.py +++ b/generators/generator_utils.py @@ -21,6 +21,7 @@ def generic_generate_func_impl( num_comps, temperature, REFLEXION_CHAT_INSTRUCTION: str, + REFLEXION_FEW_SHOT: str, SIMPLE_CHAT_INSTRUCTION: str, REFLEXION_COMPLETION_INSTRUCTION: str, SIMPLE_COMPLETION_INSTRUCTION: str, @@ -35,7 +36,7 @@ def generic_generate_func_impl( if model == "gpt-4" or model == "gpt-3.5-turbo": if strategy == "reflexion": - message = f"previous implementation:\n{prev_func_impl}\n\nunit tests:\n{feedback}\n\nhint:\n{self_reflection}\n\n# improved implementation\n{func_sig}" + message = f"{REFLEXION_FEW_SHOT}\n[previous impl]:\n{prev_func_impl}\n\n[unit test results from previous impl]:\n{feedback}\n\n[reflection on previous impl]:\n{self_reflection}\n\n[improved impl]:\n{func_sig}" # func_bodies is a really bad name, as it can also be just 1 string print('----------------------- SYSTEM MESSAGE -----------------------') print(REFLEXION_CHAT_INSTRUCTION) @@ -88,38 +89,31 @@ def generic_generate_internal_tests( TEST_GENERATION_COMPLETION_INSTRUCTION: str, parse_tests: Callable[[str], List[str]], is_syntax_valid: Callable[[str], bool], + is_react: bool = False ) -> List[str]: """ Generates tests for a function using a refinement technique with the number of specified commmittee members. """ if model == "gpt-4" or model == "gpt-3.5-turbo": - message = f'{TEST_GENERATION_FEW_SHOT}\n\nfunc signature:\n{func_sig}\nunit tests:' - print('----------------------- SYSTEM MESSAGE -----------------------') - print(TEST_GENERATION_CHAT_INSTRUCTION) - print('----------------------------------------------') - print(' ----------------------- USER MESSAGE -----------------------') - print(message, flush=True) - print('----------------------------------------------') - output = gpt_chat( - model, TEST_GENERATION_CHAT_INSTRUCTION, message, max_tokens=1024) + if is_react: + message = f'{TEST_GENERATION_FEW_SHOT}\n\n[func signature]:\n{func_sig}\n\n[think]:' + output = gpt_chat( + model, TEST_GENERATION_CHAT_INSTRUCTION, message, max_tokens=1024) + print(f'React test generation output: {output}') + else: + message = f'{TEST_GENERATION_FEW_SHOT}\n\nfunc signature:\n{func_sig}\nunit tests:' + output = gpt_chat( + model, TEST_GENERATION_CHAT_INSTRUCTION, message, max_tokens=1024) else: prompt = f'{TEST_GENERATION_COMPLETION_INSTRUCTION}\n\nfunc signature:\n{func_sig}\nunit tests:' output = gpt_completion(model, prompt, max_tokens=1024) all_tests = parse_tests(output) # type: ignore valid_tests = [test for test in all_tests if is_syntax_valid(test)] - # TODO: NOT SUPPORTED YET - # someone implement this - # cur_refinement_num = 0 - # while cur_refinement_num < committee_size: - # # TODO: implement - # cur_tests = ... # type: ignore - - # cur_refinement_num += 1 - print('--------------- GENERATED TESTS: ---------------') - print(valid_tests) - print('------------------------------------------------') + # n = 3 + # first_n = min(len(valid_tests), n) + # valid_tests = valid_tests[:first_n] return sample_n_random(valid_tests, max_num_tests) @@ -130,22 +124,23 @@ def generic_generate_self_reflection( model: str, SELF_REFLECTION_CHAT_INSTRUCTION: str, SELF_REFLECTION_COMPLETION_INSTRUCTION: str, + SELF_REFLECTION_FEW_SHOT: Optional[str] = None ) -> str: if model == "gpt-4" or model == "gpt-3.5-turbo": - print('----------------------- SYSTEM MESSAGE -----------------------') - print(SELF_REFLECTION_CHAT_INSTRUCTION) - print('----------------------------------------------') - print(' ----------------------- USER MESSAGE -----------------------') - print(f'{func}\n\n{feedback}\n\nExplanation:', flush=True) - print('----------------------------------------------') - reflection = gpt_chat( - model, SELF_REFLECTION_CHAT_INSTRUCTION, f'{func}\n\n{feedback}\n\nExplanation:') + if SELF_REFLECTION_FEW_SHOT is not None: + reflection = gpt_chat( + model, + SELF_REFLECTION_CHAT_INSTRUCTION, + f'{SELF_REFLECTION_FEW_SHOT}\n\n[function impl]:\n{func}\n\n[unit test results]:\n{feedback}\n\n[self-reflection]:') + print(f'Self reflection output: {reflection}') + else: + reflection = gpt_chat( + model, + SELF_REFLECTION_CHAT_INSTRUCTION, + f'Function implementation:\n{func}\n\nUnit test results:\n{feedback}\n\nSelf-reflection:') else: reflection = gpt_completion( model, f'{SELF_REFLECTION_COMPLETION_INSTRUCTION}\n{func}\n\n{feedback}\n\nExplanation:') - print('--------------- GENERATED SELF REFLECTION: ---------------') - print(reflection) - print('----------------------------------------------------------') return reflection # type: ignore diff --git a/generators/py_generate.py b/generators/py_generate.py index 2004c63..17cc5ce 100644 --- a/generators/py_generate.py +++ b/generators/py_generate.py @@ -9,12 +9,203 @@ PY_SIMPLE_COMPLETION_INSTRUCTION = "# Write the body of this function only." PY_REFLEXION_COMPLETION_INSTRUCTION = "You are PythonGPT. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Apply the changes below by writing the body of this function only.\n\n-----" PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = "You are PythonGPT. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation.\n\n-----" -PY_SIMPLE_CHAT_INSTRUCTION = "You are PythonGPT, an AI that only responds with python code, NOT ENGLISH. You will be given a function signature and its docstring by the user. Respond only in code with correct implementation of the function. Do not include provided the docstring in your response." # The first line of your response should have 4 spaces of indentation so that it fits syntactically with the user provided signature. -PY_REFLEXION_CHAT_INSTRUCTION = "You are PythonGPT. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Apply the changes below by writing the body of this function only. You should fill in the following text of the missing function body. For example, the first line of the completion should have 4 spaces for the indendation so that it fits syntactically with the preceding signature." -PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are PythonGPT. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation." +PY_SIMPLE_CHAT_INSTRUCTION = "You are PythonGPT, an AI that only responds with only python code. You will be given a function signature and its docstring by the user. Respond only in code with a correct, efficient implementation of the function. Do not include provided the docstring in your response." # The first line of your response should have 4 spaces of indentation so that it fits syntactically with the user provided signature. +PY_REFLEXION_CHAT_INSTRUCTION = "You are PythonGPT. You will be given your previous implementation of a function, a series of unit tests results, and your self-reflection on your previous implementation. Apply the necessary changes below by responding only with the improved body of the function. Do not include the signature in your response. The first line of your response should have 4 spaces of indentation so that it fits syntactically with the user provided signature. You will be given a few examples by the user." +PY_REFLEXION_FEW_SHOT_ADD = '''Example 1: +[previous impl]: +def add(a: int, b: int) -> int: + """ + Given integers a and b, return the total value of a and b. + """ + return a - b -PY_TEST_GENERATION_FEW_SHOT = """For example: +[unit test results from previous impl]: +Tested passed: +Tests failed: +assert add(1, 2) == 3 # output: -1 +assert add(1, 2) == 4 # output: -1 + +[reflection on previous impl]: +The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input. + +[improved impl]: +def add(a: int, b: int) -> int: + """ + Given integers a and b, return the total value of a and b. + """ + return a + b +''' + +PY_REFLEXION_FEW_SHOT = '''Example 1: +[previous impl]: +from typing import * +def fullJustify(words: List[str], maxWidth: int) -> List[str]: + """ + Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified. + You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters. + Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right. + For the last line of text, it should be left justified and no extra space is inserted between words. + Note: + A word is defined as a character sequence consisting of non-space characters only. + Each word's length is guaranteed to be greater than 0 and not exceed maxWidth. + The input array `words` contains at least one word. + """ + res = [] + cur_line = [] + cur_len = 0 + + for word in words: + if cur_len + len(word) + len(cur_line) > maxWidth: + if len(cur_line) == 1: + res.append(cur_line[0] + ' ' * (maxWidth - cur_len)) + else: + spaces = maxWidth - cur_len + space_between = spaces // (len(cur_line) - 1) + extra_spaces = spaces % (len(cur_line) - 1) + line = '' + for i, w in enumerate(cur_line[:-1]): + line += w + ' ' * (space_between + (i < extra_spaces)) + line += cur_line[-1] + res.append(line) + cur_line = [] + cur_len = 0 + cur_line.append(word) + cur_len += len(word) + + last_line = ' '.join(cur_line) + last_line += ' ' * (maxWidth - len(last_line)) + res.append(last_line) + + return res + +[unit test results from previous impl]: +Tested passed: + +Tests failed: +assert fullJustify([], 10) == [] # output: [' '] +assert fullJustify([], 0) == [] # output: [''] + +[reflection on previous impl]: +The implementation failed the test cases where the input list of words is empty. The issue arises because the code does not handle the case where there are no words to process. As a result, it still appends a line with spaces to the result list, even when there are no words. To fix this issue, we should add a condition at the beginning of the function to check if the input list is empty, and return an empty list if it is. This will ensure that the function returns the correct output for empty input lists. + +[improved impl]: +from typing import * +def fullJustify(words: List[str], maxWidth: int) -> List[str]: + """ + Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified. + You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters. + Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right. + For the last line of text, it should be left justified and no extra space is inserted between words. + Note: + A word is defined as a character sequence consisting of non-space characters only. + Each word's length is guaranteed to be greater than 0 and not exceed maxWidth. + The input array `words` contains at least one word. + """ + if not words: + return [] + + res = [] + cur_line = [] + cur_len = 0 + + for word in words: + if cur_len + len(word) + len(cur_line) > maxWidth: + if len(cur_line) == 1: + res.append(cur_line[0] + ' ' * (maxWidth - cur_len)) + else: + spaces = maxWidth - cur_len + space_between = spaces // (len(cur_line) - 1) + extra_spaces = spaces % (len(cur_line) - 1) + line = '' + for i, w in enumerate(cur_line[:-1]): + line += w + ' ' * (space_between + (i < extra_spaces)) + line += cur_line[-1] + res.append(line) + cur_line = [] + cur_len = 0 + cur_line.append(word) + cur_len += len(word) + + last_line = ' '.join(cur_line) + last_line += ' ' * (maxWidth - len(last_line)) + res.append(last_line) + + return res +END EXAMPLES + +''' + +PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are PythonGPT. You will be given a function implementation and a series of unit test results. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as guidance when you try again later. Only provide the few sentence description in your answer, not the implementation. You will be given a few examples by the user." +PY_SELF_REFLECTION_FEW_SHOT = """Example 1: +[function impl]: +def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]: + n = len(nums) + left, right = 0, 0 + max_length = 0 + current_sum = 0 + result = [] + while right < n: + current_sum += nums[right] + while current_sum > target: + current_sum -= nums[left] + left += 1 + if right - left + 1 >= max_length: + max_length = right - left + 1 + result = nums[left:right+1] + right += 1 + return result +[unit test results]: +Tests passing: +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] +assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] +assert longest_subarray_with_sum_limit([], 10) == [] +assert longest_subarray_with_sum_limit([], 0) == [] +assert longest_subarray_with_sum_limit([], -5) == [] +Tests failing: +assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: [5] +[self-reflection]: +The implementation failed the where no subarray fulfills the condition. The issue in the implementation is due to the use of >= instead of > in the condition to update the result. Because of this, it returns a subarray even when the sum is greater than the target, as it still updates the result when the current subarray length is equal to the previous longest subarray length. To overcome this error, we should change the condition to only update the result when the current subarray length is strictly greater than the previous longest subarray length. This can be done by replacing >= with > in the condition. + +Example 2: +[function impl]: +def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]: + n = len(nums) + left, right = 0, 0 + max_length = 0 + current_sum = 0 + result = [] + while current_sum + nums[right] <= target: + current_sum += nums[right] + right += 1 + while right < n: + current_sum += nums[right] + while current_sum > target: + current_sum -= nums[left] + left += 1 + if right - left + 1 > max_length: + max_length = right - left + 1 + result = nums[left:right+1] + right += 1 + return result +[unit test results]: +Tests passing: +assert longest_subarray_with_sum_limit([], 10) == [] +assert longest_subarray_with_sum_limit([], 0) == [] +assert longest_subarray_with_sum_limit([], -5) == [] +Tests failing: +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] # output: list index out of range +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] # output: list index out of range +assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: list index out of range +assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] # output: list index out of range +[self-reflection]: +The implementation failed 4 out of the 7 test cases due to an IndexError. The issue stems from the while loop while current_sum + nums[right] <= target:, which directly accesses nums[right] without checking if right is within the bounds of the list. This results in a runtime error when right goes beyond the list length. To overcome this error, we need to add a bounds check for the right variable in the mentioned while loop. We can modify the loop condition to while right < len(nums) and current_sum + nums[right] <= target:. This change will ensure that we only access elements within the bounds of the list, thus avoiding the IndexError. +END OF EXAMPLES + +""" + +PY_TEST_GENERATION_FEW_SHOT = """Examples: func signature: def has_close_elements(numbers: List[float], threshold: float) -> bool: \"\"\" Check if in given list of numbers, are any two numbers closer to each other than @@ -24,7 +215,6 @@ def has_close_elements(numbers: List[float], threshold: float) -> bool: >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True \"\"\" - unit tests: assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False @@ -34,11 +224,36 @@ assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False""" -PY_TEST_GENERATION_COMPLETION_INSTRUCTION = f"""You are PythonGPT, an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. +PY_TEST_GENERATION_FEW_SHOT_REACT = '''Example 1: +[func signature]: +def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]: + """ + Given a list of integers nums and an integer target, write a function that returns the longest contiguous subarray of nums with a sum less than or equal to target. + If there is no subarray with sum less than or equal to target, return an empty list. + """ +[think]: +Okay, let's think of some test cases for this longest_subarray_with_sum_limit function. We need to cover various edge cases. First, let's test with a simple list of positive integers and a target value where a subarray exists. Next, we could test with a target value greater than the sum of all elements in the list, which should return the entire list. We should also test a case where no subarray fulfills the condition, so the function should return an empty list. It would be interesting to include negative numbers in the list and test if the function handles them correctly. Finally, we should also test with an empty list and different target values to make sure the function can handle those cases. +[unit tests]: +# Test case 1: Simple list of positive integers with existing subarray +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] +# Test case 2: Target value greater than sum of all elements in the list +assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] +# Test case 3: No subarray fulfills the condition +assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] +# Test case 4: List with negative numbers +assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] +# Test case 5: Empty list with different target values +assert longest_subarray_with_sum_limit([], 10) == [] +assert longest_subarray_with_sum_limit([], 0) == [] +assert longest_subarray_with_sum_limit([], -5) == [] +END OF EXAMPLES +''' +PY_TEST_GENERATION_COMPLETION_INSTRUCTION = f"""You are PythonGPT, an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring. {PY_TEST_GENERATION_FEW_SHOT}""" PY_TEST_GENERATION_CHAT_INSTRUCTION = """You are CodexGPT, an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring.""" +PY_TEST_GENERATION_CHAT_INSTRUCTION_REACT = """You are CodexGPT, an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the description and signature. You will first 'think', brainstorming possible edge cases and points of failure. Then, you will write a series of unit tests that reflect your thinking. Make sure that tests conform to any constraints. You will be given a few examples.""" class PyGenerator(Generator): def self_reflection(self, func: str, feedback: str, model: str) -> str: @@ -48,6 +263,7 @@ class PyGenerator(Generator): model=model, SELF_REFLECTION_CHAT_INSTRUCTION=PY_SELF_REFLECTION_CHAT_INSTRUCTION, SELF_REFLECTION_COMPLETION_INSTRUCTION=PY_SELF_REFLECTION_COMPLETION_INSTRUCTION, + SELF_REFLECTION_FEW_SHOT=PY_SELF_REFLECTION_FEW_SHOT ) return x @@ -72,6 +288,7 @@ class PyGenerator(Generator): num_comps=num_comps, temperature=temperature, REFLEXION_CHAT_INSTRUCTION=PY_REFLEXION_CHAT_INSTRUCTION, + REFLEXION_FEW_SHOT = PY_REFLEXION_FEW_SHOT_ADD, SIMPLE_CHAT_INSTRUCTION=PY_SIMPLE_CHAT_INSTRUCTION, REFLEXION_COMPLETION_INSTRUCTION=PY_REFLEXION_COMPLETION_INSTRUCTION, SIMPLE_COMPLETION_INSTRUCTION=PY_SIMPLE_COMPLETION_INSTRUCTION, diff --git a/reflexion.py b/reflexion.py index b606fdb..a31fc05 100644 --- a/reflexion.py +++ b/reflexion.py @@ -59,7 +59,7 @@ def run_reflexion( strategy="reflexion", prev_func_impl=cur_func_impl, feedback=cur_feedback, - self_reflection=reflection + self_reflection=reflection, ) assert isinstance(cur_func_impl, str) diff --git a/root/reflexion_leetcode_python3_gpt4_react/leetcode-hard-py-40-uncontaminated._reflexion_5_gpt-4_pass_at_k_1_py.jsonl b/root/reflexion_leetcode_python3_gpt4_react/leetcode-hard-py-40-uncontaminated._reflexion_5_gpt-4_pass_at_k_1_py.jsonl new file mode 100644 index 0000000..f062a7e --- /dev/null +++ b/root/reflexion_leetcode_python3_gpt4_react/leetcode-hard-py-40-uncontaminated._reflexion_5_gpt-4_pass_at_k_1_py.jsonl @@ -0,0 +1,2 @@ +{"task_id": "minimum-reverse-operations", "prompt": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n \"\"\"\n", "entry_point": "minimum-reverse-operations", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, indicating that the logic for calculating the minimum number of reverse operations is incorrect. The issue lies in the calculation of the operations and the conditions used to determine if a position can be reached. The current implementation checks if the distance between the target position and the initial position is divisible by k and if the number of operations is even. However, this approach does not account for all possible scenarios and fails to find valid solutions in some cases. To overcome this error, we need to reevaluate the logic for calculating the minimum number of reverse operations and consider all possible ways to reach a target position while adhering to the constraints of the problem.", "The implementation failed 4 out of the 5 test cases. The issue lies in the calculation of the number of reverse operations and the validation of the operations. The current approach checks if the distance between the target position and the initial position is divisible by k and then calculates the operations. However, this approach does not account for the cases where the 1 needs to be moved to a position that is not directly reachable by reversing subarrays of size k. To overcome this error, we need to modify the algorithm to consider all possible ways to reach the target position, taking into account the banned positions and the subarray size k. This may involve changing the way we calculate the number of operations and validating the operations to ensure that the 1 does not end up in a banned position.", "The implementation failed 4 out of the 5 test cases, indicating that there is an issue with the logic in the is_valid_move function. The function is not correctly determining if it's possible to move the 1 to the desired position using the given number of reverse operations. The current implementation checks if the difference between start and end positions is divisible by k and if any banned positions are encountered during the steps. However, this approach does not account for all possible scenarios and leads to incorrect results. To fix this issue, we need to reevaluate the logic in the is_valid_move function and ensure that it accurately determines the feasibility of moving the 1 to the desired position using the given number of reverse operations while avoiding banned positions.", "The implementation failed 4 out of the 5 test cases, indicating that the logic for determining the minimum number of reverse operations is incorrect. The issue lies in the is_valid_move function, which checks if it's possible to move the 1 from position p to position i. The current implementation does not correctly account for all possible valid moves, leading to incorrect results. To fix this issue, we need to reevaluate the logic for determining valid moves and ensure that it correctly handles all possible cases, including the cases where the 1 needs to be moved through multiple subarrays of size k. Additionally, we should verify that the calculation of the minimum number of reverse operations is accurate for each valid move."], "solution": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n \"\"\"\n ans = [-1] * n\n banned_set = set(banned)\n\n def is_valid_move(start, end, k, banned_set):\n if (start - end) % k != 0:\n return False\n steps = abs(start - end) // k\n for i in range(steps):\n current_position = start + (2 * i - steps + 1) * k\n if current_position % (2 * k) in banned_set:\n return False\n return True\n\n def min_operations(start, end, k, banned_set):\n if start == end:\n return 0\n if (start - end) % k != 0:\n return float('inf')\n steps = abs(start - end) // k\n operations = 0\n for i in range(steps):\n current_position = start + (2 * i - steps + 1) * k\n if current_position % (2 * k) in banned_set:\n return float('inf')\n operations += 1\n return operations\n\n for i in range(n):\n if i == p or i in banned_set:\n continue\n\n operations = min_operations(p, i, k, banned_set)\n if operations != float('inf'):\n ans[i] = operations\n\n ans[p] = 0\n return ans"} +{"task_id": "collect-coins-in-a-tree", "prompt": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n \"\"\"\n", "entry_point": "collect-coins-in-a-tree", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the dfs function, which does not account for the possibility of starting at a vertex with no coins. This results in an incorrect minimum number of moves calculation. Additionally, the implementation does not handle the case where all vertices have no coins, returning 'inf' instead of 0. To overcome these errors, we need to modify the dfs function to account for starting at vertices with no coins and handle the case where all vertices have no coins. This can be done by updating the initial value of min_moves to 0 when there are no coins in the tree and adjusting the dfs function to consider starting at vertices with no coins.", "The implementation failed 2 out of the 5 test cases, producing incorrect results for certain tree structures. The issue lies in the dfs function, which does not account for the optimal path to collect all coins. It calculates the total moves by simply adding 2 for each coin found in the neighbors, without considering the possibility of collecting coins from nodes at a distance of 2 from the current node. To overcome this error, we need to modify the dfs function to account for the optimal path to collect all coins, considering the coins that can be collected from nodes at a distance of 2 from the current node. This can be done by updating the dfs function to explore all possible paths and choose the one with the minimum number of moves required to collect all coins and return to the initial vertex.", "The implementation failed 5 out of the 5 test cases, producing incorrect results and even negative values. The issue lies in the dfs function, where it calculates the total_moves incorrectly. The current implementation adds the two largest child_moves values, but it does not account for the fact that we need to return to the initial vertex after collecting all the coins. Additionally, the condition to update min_moves is not sufficient, as it only checks if the current node has a coin or if the sum of coins is 0. To fix these issues, we need to reevaluate the logic in the dfs function and ensure that the min_moves calculation takes into account the requirement to return to the initial vertex. Furthermore, we should revise the condition for updating min_moves to better reflect the problem constraints."], "solution": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n \"\"\"\n from collections import defaultdict\n\n def dfs(node: int, parent: int) -> Tuple[int, int]:\n total_moves = 0\n child_moves = []\n coins_collected = coins[node]\n\n for neighbor in tree[node]:\n if neighbor != parent:\n moves, collected = dfs(neighbor, node)\n total_moves += moves\n coins_collected += collected\n if coins[neighbor] == 1:\n child_moves.append(2)\n\n if coins_collected > 0:\n child_moves.sort(reverse=True)\n for i in range(min(2, len(child_moves))):\n total_moves += child_moves[i]\n\n return total_moves, coins_collected\n\n tree = defaultdict(list)\n for edge in edges:\n tree[edge[0]].append(edge[1])\n tree[edge[1]].append(edge[0])\n\n min_moves = float('inf') if sum(coins) > 0 else 0\n for i in range(len(coins)):\n moves, collected = dfs(i, -1)\n if collected == sum(coins):\n min_moves = min(min_moves, moves)\n\n return min_moves"} diff --git a/root/reflexion_leetcode_python3_gpt4_react_constraints/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl b/root/reflexion_leetcode_python3_gpt4_react_constraints/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl new file mode 100644 index 0000000..4c6286d --- /dev/null +++ b/root/reflexion_leetcode_python3_gpt4_react_constraints/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl @@ -0,0 +1,34 @@ +{"task_id": "minimum-reverse-operations", "prompt": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n", "entry_point": "minimum-reverse-operations", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, as it returned incorrect results for all of them. The issue lies in the calculation of the minimum number of reverse operations needed to bring the 1 to position i in the array. The current implementation only checks if the difference between i and p is divisible by k and if the steps are even. However, this approach does not account for the possibility of reversing subarrays multiple times to reach the desired position. To overcome this error, we need to modify the logic to consider all possible ways of reaching position i using reverse operations, while also ensuring that the 1 does not go to any of the banned positions. This may require a more comprehensive approach, such as using dynamic programming or breadth-first search, to find the minimum number of reverse operations needed for each position.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or -1 for positions that should be reachable. The issue lies in the logic for calculating the new position after reversing the subarray. The current approach does not guarantee that the 1 will be moved to the correct position after reversing the subarray. Additionally, the loop for generating new positions does not consider all possible subarrays that can be reversed. To fix these issues, we need to reevaluate the logic for calculating new positions and ensure that all possible subarrays are considered when reversing. This may involve changing the loop structure and the way new positions are calculated after reversing the subarray.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or -1 values where it should have been possible to move the 1 to the desired position. The issue lies in the logic of the while loop and the way new positions are calculated. The current implementation does not take into account the constraints of the subarray size k and the banned positions when calculating new positions. This leads to incorrect results and an inability to find valid paths to move the 1 to the desired position. To overcome this error, we need to reevaluate the logic for calculating new positions and ensure that the constraints of subarray size k and banned positions are properly considered.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or -1 values where it should have returned the minimum number of reverse operations. The issue lies in the logic of the while loop and the way new positions are calculated. The current implementation does not take into account the constraints of the problem, such as the fact that the 1 in the array should never go to any of the positions in banned. Additionally, the new position calculation does not consider the size of the subarray (k) properly, leading to incorrect results. To fix this issue, we need to reevaluate the logic for calculating new positions and ensure that the constraints of the problem are properly taken into account when performing reverse operations."], "solution": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n banned_set = set(banned)\n ans = [-1] * n\n ans[p] = 0\n\n for i in range(n):\n if i not in banned_set:\n queue = [(p, 0)]\n visited = set()\n\n while queue:\n cur_pos, steps = queue.pop(0)\n\n if cur_pos == i:\n ans[i] = steps\n break\n\n for j in range(n - k + 1):\n if j <= cur_pos < j + k:\n new_pos = j + (j + k - 1 - cur_pos)\n else:\n continue\n\n if new_pos not in visited and new_pos not in banned_set:\n visited.add(new_pos)\n queue.append((new_pos, steps + 1))\n\n return ans"} +{"task_id": "collect-coins-in-a-tree", "prompt": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n", "entry_point": "collect-coins-in-a-tree", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 5 test cases. The issue lies in the dfs function, which does not account for the fact that we can collect coins at a distance of at most 2 from the current vertex. The current implementation only considers collecting coins from direct neighbors, which leads to an incorrect calculation of the minimum number of edges needed. To overcome this error, we need to modify the dfs function to consider collecting coins from vertices that are at a distance of 2 from the current vertex. This can be done by updating the dfs function to traverse the tree up to a depth of 2 and accumulate the total moves accordingly.", "The implementation failed 5 out of the 5 test cases, producing incorrect results for all of them. The issue lies in the dfs function, which does not account for the fact that the starting vertex can be any vertex in the tree. The current implementation only starts from vertices with coins, which may not always result in the minimum number of moves. Additionally, the dfs function does not properly handle the case when the starting vertex has a coin, as it does not count the moves required to return to the starting vertex. To fix these issues, we need to modify the dfs function to start from any vertex and account for the moves required to return to the starting vertex when it has a coin. This can be done by iterating through all vertices as starting points and updating the min_moves variable accordingly.", "The implementation failed 5 out of the 5 test cases, indicating that the logic for calculating the minimum number of edges to collect all coins and return to the initial vertex is incorrect. The issue lies in the dfs function, which does not account for the optimal path to collect coins at a distance of 2 from the current vertex. Additionally, the implementation does not consider the possibility of starting at different vertices to minimize the number of moves. To overcome these errors, we need to reevaluate the logic for traversing the tree and collecting coins, taking into account the optimal path and starting vertex.", "The implementation failed 2 out of the 5 test cases. The issue lies in the fact that the function always starts the DFS traversal from node 0, which may not be the optimal starting point to collect all the coins. This leads to an incorrect calculation of the minimum number of edges required to collect all the coins and return to the initial vertex. To overcome this error, we need to find an optimal starting point for the DFS traversal, which can be done by first identifying a node with a coin and then starting the traversal from that node. This change will ensure that the function calculates the minimum number of edges correctly for all cases."], "solution": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n from collections import defaultdict\n from typing import Tuple\n\n def dfs(node: int, parent: int) -> Tuple[int, int]:\n total_moves = 0\n max_gain = 0\n\n for neighbor in tree[node]:\n if neighbor != parent:\n moves, gain = dfs(neighbor, node)\n total_moves += moves\n max_gain = max(max_gain, gain - moves)\n\n if coins[node] == 1:\n total_moves += 2\n max_gain += 2\n\n return total_moves, max_gain\n\n tree = defaultdict(list)\n for edge in edges:\n tree[edge[0]].append(edge[1])\n tree[edge[1]].append(edge[0])\n\n starting_node = coins.index(1)\n moves, gain = dfs(starting_node, -1)\n min_moves = moves - gain\n\n return min_moves"} +{"task_id": "minimum-time-to-visit-a-cell-in-a-grid", "prompt": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n", "entry_point": "minimum-time-to-visit-a-cell-in-a-grid", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the fact that the visited cells are marked as visited before checking if the new_time is valid for that cell. This causes the algorithm to consider invalid paths and return incorrect results. To overcome this error, we should only mark a cell as visited if the new_time is greater than or equal to the cell's value. This can be done by moving the visited[new_row][new_col] = True line inside the condition where new_time is checked against grid[new_row][new_col]. By doing this, we ensure that only valid paths are considered, and the algorithm returns the correct minimum time required to visit the bottom-right cell.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the new_time variable and push it into the priority queue. The current implementation updates new_time with the maximum of time + 1 and grid[new_row][new_col], even when the new_time is less than the current time. This causes the algorithm to overestimate the time required to reach certain cells, leading to incorrect results. To fix this issue, we should only update new_time and push it into the priority queue if the new_time is greater than or equal to the current time. This can be done by adding a condition to check if new_time >= time before updating the visited matrix and pushing the new_time into the priority queue.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the visited matrix and push the new position into the queue. The current implementation checks if new_time is greater than or equal to the current time, which allows visiting cells with a higher time requirement than necessary. To fix this issue, we should change the condition to check if new_time is greater than or equal to the grid value at the new position. This will ensure that we only visit cells when the time requirement is met, and it will prevent overestimating the minimum time required to reach the bottom-right cell.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the visited cells and push them into the priority queue. The current implementation checks if new_time >= grid[new_row][new_col] before marking the cell as visited and pushing it into the queue. However, this condition allows visiting cells with a time greater than the required minimum time, leading to incorrect results. To fix this issue, we should only mark the cell as visited and push it into the queue if the new_time is exactly equal to grid[new_row][new_col]. This can be done by changing the condition to if new_time == grid[new_row][new_col]:. This change will ensure that we only visit cells at the minimum required time, thus providing the correct results."], "solution": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n from heapq import heappush, heappop\n\n m, n = len(grid), len(grid[0])\n directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n visited = [[False] * n for _ in range(m)]\n visited[0][0] = True\n queue = [(0, 0, 0)]\n\n while queue:\n time, row, col = heappop(queue)\n if row == m - 1 and col == n - 1:\n return time\n\n for dr, dc in directions:\n new_row, new_col = row + dr, col + dc\n if 0 <= new_row < m and 0 <= new_col < n and not visited[new_row][new_col]:\n new_time = max(time + 1, grid[new_row][new_col])\n if new_time == grid[new_row][new_col]:\n visited[new_row][new_col] = True\n heappush(queue, (new_time, new_row, new_col))\n\n return -1"} +{"task_id": "find-the-string-with-lcp", "prompt": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n", "entry_point": "find-the-string-with-lcp", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 4 out of the 5 test cases, producing incorrect results for various lcp matrices. The issue lies in the nested loop where the function updates the characters in the word list based on the lcp values. The current approach does not guarantee the lexicographically smallest string, as it only updates the character at the lcp[i][j] index without considering the impact on the rest of the string. To overcome this error, we need to revise the algorithm to ensure that the generated string is lexicographically smallest while still satisfying the lcp constraints. This may involve rethinking the approach to updating the characters in the word list and considering the impact of each update on the overall string.", "The implementation failed 4 out of the 5 test cases, returning empty strings instead of the expected results. The issue lies in the nested loops and the conditions used to update the characters in the 'word' list. The current implementation does not correctly handle the cases where the lcp values are smaller than the length of the string. Additionally, the check for the 'z' character and the condition to return an empty string are not properly implemented, causing the function to return an empty string in most cases. To fix these issues, we need to revise the logic for updating the characters in the 'word' list and ensure that the conditions for returning an empty string are correctly applied.", "The implementation failed 4 out of the 5 test cases, returning empty strings instead of the expected results. The issue lies in the nested loops that update the word list and the subsequent check for the lcp matrix. The current implementation does not correctly handle the construction of the word list based on the lcp matrix, and the final check for the lcp matrix is not accurate. To fix this issue, we need to revise the logic for constructing the word list and ensure that it satisfies the given lcp matrix constraints. Additionally, we should improve the final check for the lcp matrix to ensure that the constructed word list is valid.", "The implementation failed 4 out of the 5 test cases, returning empty strings instead of the expected results. The issue lies in the construction of the word from the lcp matrix. The current approach updates the characters in the word based on the lcp values, but it does not guarantee the lexicographically smallest string. To overcome this error, we need to revise the algorithm to construct the word in a way that ensures the lexicographically smallest string while satisfying the lcp constraints. This may involve rethinking the approach to updating the characters in the word and ensuring that the constructed word satisfies the given lcp matrix."], "solution": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n from typing import List\n\n n = len(lcp)\n word = ['a'] * n\n\n for i in range(n):\n for j in range(i + 1, n):\n if lcp[i][j] < n:\n if word[lcp[i][j]] == 'z':\n return \"\"\n word[lcp[i][j]] = chr(ord(word[lcp[i][j]]) + 1)\n\n def check_lcp(word: List[str], lcp: List[List[int]]) -> bool:\n for i in range(n):\n for j in range(i + 1, n):\n common_prefix = 0\n for k in range(n):\n if word[i + k] == word[j + k]:\n common_prefix += 1\n else:\n break\n if common_prefix != lcp[i][j]:\n return False\n return True\n\n if check_lcp(word, lcp):\n return \"\".join(word)\n else:\n return \"\""} +{"task_id": "handling-sum-queries-after-update", "prompt": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n", "entry_point": "handling-sum-queries-after-update", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 4 out of the 5 test cases. The issue lies in the handling of type 1 queries, where the function is supposed to flip the values in nums1 from index l to index r. The current implementation flips the values from index l to index r inclusive, which is incorrect. To fix this issue, we need to change the range in the for loop for type 1 queries to exclude the r index. We can do this by modifying the range to range(l, r) instead of range(l, r + 1). This change will ensure that the values are flipped correctly, and the function should produce the expected results for all test cases.", "The implementation failed all the test cases involving type 1 queries. The issue is in the loop for type 1 queries, where the range is defined as range(l, r) instead of range(l, r+1). This causes the loop to exclude the element at index r, leading to incorrect flipping of values in nums1. To fix this error, we need to change the range to range(l, r+1) in the loop for type 1 queries. This will ensure that all elements from index l to index r are included in the flipping process, resulting in correct handling of type 1 queries.", "The implementation failed 4 out of the 5 test cases. The issue lies in the handling of type 1 queries, where the function is supposed to flip the values of nums1 from index l to index r. The current implementation flips the values correctly, but it does not update nums2 accordingly. As a result, the sum of nums2 in type 3 queries is incorrect. To fix this issue, we need to update nums2 whenever we flip a value in nums1 during type 1 queries. We can do this by subtracting the original value of nums1[i] from nums2[i] and then adding the flipped value of nums1[i] to nums2[i]. This will ensure that nums2 is updated correctly, and the sum of nums2 in type 3 queries will be accurate.", "The implementation failed all the test cases involving type 1 and type 2 queries. The issue in the implementation is that it updates nums2 during type 1 queries, which is unnecessary and leads to incorrect results. Instead, nums2 should only be updated during type 2 queries. To fix this error, we should remove the nums2 update lines from the type 1 query block and only update nums1. This will ensure that nums2 is only updated when required, and the results for type 3 queries will be accurate."], "solution": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n result = []\n \n for query in queries:\n if query[0] == 1:\n l, r = query[1], query[2]\n for i in range(l, r+1):\n nums1[i] = 1 - nums1[i]\n elif query[0] == 2:\n p = query[1]\n for i in range(len(nums1)):\n nums2[i] += nums1[i] * p\n elif query[0] == 3:\n result.append(sum(nums2))\n \n return result"} +{"task_id": "subsequence-with-the-minimum-score", "prompt": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n", "entry_point": "subsequence-with-the-minimum-score", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 4 test cases, producing incorrect minimum scores. The issue lies in the dynamic programming approach used, which calculates the minimum score based on the number of characters removed from the string t. However, this approach does not account for the relative positions of the remaining characters in the string s, which is crucial for determining if t is a subsequence of s. As a result, the calculated minimum scores are incorrect. To overcome this error, we need to modify the algorithm to consider the relative positions of the characters in both strings while calculating the minimum score. This can be achieved by using a two-pointer approach or a different dynamic programming approach that takes into account the relative positions of the characters in both strings.", "The implementation failed 3 out of the 4 test cases, producing incorrect results. The issue lies in the calculation of the minimum score. The current implementation calculates the score using the difference between the indices i and j, which does not accurately represent the score as defined in the problem statement. To fix this issue, we need to modify the approach to track the left and right indices of the removed characters and calculate the score based on these indices. This can be done by iterating through the dp table and finding the minimum score for each valid subsequence, considering the left and right indices of the removed characters.", "The implementation failed 3 out of the 4 test cases, producing incorrect results or negative scores. The issue lies in the calculation of the minimum score. The current implementation calculates the left and right indices incorrectly, leading to an incorrect score calculation. Additionally, the condition to update the minimum score is not properly set, as it only checks if dp[i][j] == m, which may not always be the case for a valid subsequence. To fix these issues, we need to revise the logic for calculating the left and right indices and update the condition for updating the minimum score. This will ensure that the correct minimum score is calculated for all test cases.", "The implementation failed 5 out of the 5 test cases, with incorrect outputs and IndexError. The issue stems from the nested loops iterating over the entire dp matrix and the while loop inside the loops, which tries to access elements in the strings s and t without proper bounds checks. This results in runtime errors and incorrect calculations for the minimum score. To overcome these errors, we need to revise the logic for finding the minimum score and ensure proper bounds checks when accessing elements in the strings s and t. Additionally, the current implementation does not handle the case where no characters are removed from the string t, resulting in incorrect outputs for some test cases."], "solution": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n n, m = len(s), len(t)\n dp = [[0] * (m + 1) for _ in range(n + 1)]\n\n for i in range(1, n + 1):\n for j in range(1, m + 1):\n if s[i - 1] == t[j - 1]:\n dp[i][j] = dp[i - 1][j - 1] + 1\n else:\n dp[i][j] = dp[i - 1][j]\n\n min_score = float('inf')\n for i in range(n + 1):\n if dp[i][m] > 0:\n left = i - dp[i][m]\n right = i - 1\n j = m\n while j > 0 and right >= 0:\n if s[right] == t[j - 1]:\n j -= 1\n right -= 1\n min_score = min(min_score, right - left + 1)\n\n return min_score if min_score != float('inf') else 0"} +{"task_id": "minimum-number-of-visited-cells-in-a-grid", "prompt": "def minimumVisitedCells(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a 0-indexed m x n integer matrix grid. Your initial position is at the top-left cell (0, 0).\n Starting from the cell (i, j), you can move to one of the following cells:\n Cells (i, k) with j < k <= grid[i][j] + j (rightward movement), or\n Cells (k, j) with i < k <= grid[i][j] + i (downward movement).\n Return the minimum number of cells you need to visit to reach the bottom-right cell (m - 1, n - 1). If there is no valid path, return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 1 <= m, n <= 105\n 1 <= m * n <= 105\n 0 <= grid[i][j] < m * n\n grid[m - 1][n - 1] == 0\n \"\"\"\n", "entry_point": "minimum-number-of-visited-cells-in-a-grid", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the test cases, producing incorrect results. The issue lies in the use of a priority queue (heap) and the visited set. The visited set prevents revisiting cells, but it does not account for the possibility of reaching a cell with a smaller number of visited cells. This leads to suboptimal paths being considered as the final result. To overcome this error, we should use a different approach, such as dynamic programming or BFS, to ensure that we explore all possible paths and find the minimum number of visited cells required to reach the bottom-right cell.", "The implementation failed 5 out of the test cases, producing incorrect results. The issue lies in the way the visited cells are being counted. The current implementation counts the number of cells visited in the path, but the problem asks for the minimum number of cells needed to visit to reach the destination. This means that we should not count the cells that are visited more than once. To fix this issue, we need to change the way we count the visited cells, ensuring that we only count the unique cells visited in the path. Additionally, we should also verify if the problem constraints are being met, such as grid[m - 1][n - 1] == 0, to ensure the correct output.", "The implementation failed 3 out of the 5 test cases, producing incorrect results for cases where a valid path exists. The issue lies in the way the steps are being counted. The current implementation increments the steps for each cell visited, which results in an overcounting of the steps. Instead, we should increment the steps only when we move from one cell to another. To fix this issue, we can change the way we append new cells to the queue. Instead of appending (k, j, steps + 1) and (i, k, steps + 1), we should append (k, j, steps) and (i, k, steps). This will ensure that we only count the steps when we actually move from one cell to another, giving us the correct minimum number of cells visited.", "The implementation failed 3 out of the 5 test cases, returning incorrect results for cases where a valid path exists. The issue lies in the while loop where we append new cells to the queue. We are not updating the steps variable when appending new cells, which causes the steps to remain constant throughout the traversal. To fix this error, we need to increment the steps variable when appending new cells to the queue. We can do this by changing queue.append((i, k, steps)) to queue.append((i, k, steps + 1)) and queue.append((k, j, steps)) to queue.append((k, j, steps + 1)). This change will ensure that the steps variable is updated correctly, and the function returns the correct minimum number of cells visited."], "solution": "def minimumVisitedCells(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a 0-indexed m x n integer matrix grid. Your initial position is at the top-left cell (0, 0).\n Starting from the cell (i, j), you can move to one of the following cells:\n Cells (i, k) with j < k <= grid[i][j] + j (rightward movement), or\n Cells (k, j) with i < k <= grid[i][j] + i (downward movement).\n Return the minimum number of cells you need to visit to reach the bottom-right cell (m - 1, n - 1). If there is no valid path, return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 1 <= m, n <= 105\n 1 <= m * n <= 105\n 0 <= grid[i][j] < m * n\n grid[m - 1][n - 1] == 0\n \"\"\"\n from collections import deque\n\n m, n = len(grid), len(grid[0])\n if grid[m - 1][n - 1] != 0:\n return -1\n\n visited = [[False] * n for _ in range(m)]\n visited[0][0] = True\n queue = deque([(0, 0, 1)]) # (row, col, steps)\n\n while queue:\n i, j, steps = queue.popleft()\n\n if i == m - 1 and j == n - 1:\n return steps\n\n for k in range(j + 1, min(j + grid[i][j] + 1, n)):\n if not visited[i][k]:\n visited[i][k] = True\n queue.append((i, k, steps + 1))\n\n for k in range(i + 1, min(i + grid[i][j] + 1, m)):\n if not visited[k][j]:\n visited[k][j] = True\n queue.append((k, j, steps + 1))\n\n return -1"} +{"task_id": "rearranging-fruits", "prompt": "def minCost(basket1: List[int], basket2: List[int]) -> int:\n \"\"\"\n You have two fruit baskets containing n fruits each. You are given two 0-indexed integer arrays basket1 and basket2 representing the cost of fruit in each basket. You want to make both baskets equal. To do so, you can use the following operation as many times as you want:\n Chose two indices i and j, and swap the ith fruit of basket1 with the jth fruit of basket2.\n The cost of the swap is min(basket1[i],basket2[j]).\n Two baskets are considered equal if sorting them according to the fruit cost makes them exactly the same baskets.\n Return the minimum cost to make both the baskets equal or -1 if impossible.\n Constraints:\n \n basket1.length == bakste2.length\n 1 <= basket1.length <= 105\n 1 <= basket1[i],basket2[i] <= 109\n \"\"\"\n", "entry_point": "rearranging-fruits", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases, returning -1 instead of the expected minimum cost. The issue lies in the odd_count calculation, which checks if there are any odd counts in the combined counter. This condition is too strict, as it is possible to have odd counts in the combined counter but still be able to make the baskets equal. The correct condition should check if the sum of odd counts is even, as this would allow for an equal distribution of odd-count fruits between the two baskets. To fix this error, we should change the condition to check if the sum of odd counts is even, which can be done by replacing the if statement with if odd_count % 2 != 0:. This change will ensure that the function only returns -1 when it is truly impossible to make the baskets equal."], "solution": "def minCost(basket1: List[int], basket2: List[int]) -> int:\n \"\"\"\n You have two fruit baskets containing n fruits each. You are given two 0-indexed integer arrays basket1 and basket2 representing the cost of fruit in each basket. You want to make both baskets equal. To do so, you can use the following operation as many times as you want:\n Chose two indices i and j, and swap the ith fruit of basket1 with the jth fruit of basket2.\n The cost of the swap is min(basket1[i],basket2[j]).\n Two baskets are considered equal if sorting them according to the fruit cost makes them exactly the same baskets.\n Return the minimum cost to make both the baskets equal or -1 if impossible.\n Constraints:\n \n basket1.length == bakste2.length\n 1 <= basket1.length <= 105\n 1 <= basket1[i],basket2[i] <= 109\n \"\"\"\n from collections import Counter\n\n combined = basket1 + basket2\n counter = Counter(combined)\n odd_count = sum(1 for count in counter.values() if count % 2 != 0)\n\n if odd_count % 2 != 0:\n return -1\n\n basket1.sort()\n basket2.sort()\n\n cost = 0\n i, j = 0, 0\n while i < len(basket1) and j < len(basket2):\n if basket1[i] != basket2[j]:\n cost += min(basket1[i], basket2[j])\n i += 1\n j += 1\n else:\n i += 1\n j += 1\n\n return cost"} +{"task_id": "count-increasing-quadruplets", "prompt": "def countQuadruplets(nums: List[int]) -> int:\n \"\"\"\n Given a 0-indexed integer array nums of size n containing all numbers from 1 to n, return the number of increasing quadruplets.\n A quadruplet (i, j, k, l) is increasing if:\n 0 <= i < j < k < l < n, and\n nums[i] < nums[k] < nums[j] < nums[l].\n Constraints:\n \n 4 <= nums.length <= 4000\n 1 <= nums[i] <= nums.length\n All the integers of nums are unique. nums is a permutation.\n \"\"\"\n", "entry_point": "count-increasing-quadruplets", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": [], "solution": "def countQuadruplets(nums: List[int]) -> int:\n \"\"\"\n Given a 0-indexed integer array nums of size n containing all numbers from 1 to n, return the number of increasing quadruplets.\n A quadruplet (i, j, k, l) is increasing if:\n 0 <= i < j < k < l < n, and\n nums[i] < nums[k] < nums[j] < nums[l].\n Constraints:\n \n 4 <= nums.length <= 4000\n 1 <= nums[i] <= nums.length\n All the integers of nums are unique. nums is a permutation.\n \"\"\"\n n = len(nums)\n count = 0\n\n for i in range(n):\n for j in range(i + 1, n):\n for k in range(j + 1, n):\n for l in range(k + 1, n):\n if nums[i] < nums[k] < nums[j] < nums[l]:\n count += 1\n\n return count"} +{"task_id": "put-marbles-in-bags", "prompt": "def putMarbles(weights: List[int], k: int) -> int:\n \"\"\"\n You have k bags. You are given a 0-indexed integer array weights where weights[i] is the weight of the ith marble. You are also given the integer k.\n Divide the marbles into the k bags according to the following rules:\n No bag is empty.\n If the ith marble and jth marble are in a bag, then all marbles with an index between the ith and jth indices should also be in that same bag.\n If a bag consists of all the marbles with an index from i to j inclusively, then the cost of the bag is weights[i] + weights[j].\n The score after distributing the marbles is the sum of the costs of all the k bags.\n Return the difference between the maximum and minimum scores among marble distributions.\n Constraints:\n \n 1 <= k <= weights.length <= 105\n 1 <= weights[i] <= 109\n \"\"\"\n", "entry_point": "put-marbles-in-bags", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all test cases due to a TypeError: 'float' object is not callable. The issue stems from the function name max_score being used as a variable name in the line max_score = float('-inf'). This causes a conflict between the function name and the variable name, leading to the TypeError. To overcome this error, we need to change the variable name to avoid the conflict. For example, we can change the variable name to max_current_score, and update the corresponding lines in the function. This change will resolve the conflict and prevent the TypeError.", "The implementation failed 2 out of the 5 test cases. The issue lies in the calculation of the minimum and maximum scores in the dp and max_score functions. The current implementation calculates the minimum and maximum scores by considering the sum of weights[i] and weights[j] for all possible i and j combinations. However, this approach does not guarantee that the marbles are distributed optimally among the k bags. To fix this issue, we need to modify the dp and max_score functions to consider the optimal distribution of marbles among the k bags while calculating the minimum and maximum scores. This can be achieved by using dynamic programming to find the optimal distribution of marbles and updating the memoization dictionaries accordingly.", "The implementation failed 2 out of the 5 test cases. The issue lies in the dp and max_score functions, where the range of the for loop is incorrect. The loop iterates from i to len(weights) - k + 1, which causes an overestimation of the minimum and maximum scores. To fix this error, we need to adjust the range of the for loop to correctly consider the remaining marbles and bags. We can modify the loop condition to for j in range(i, len(weights) - k + 2) in both the dp and max_score functions. This change will ensure that we correctly calculate the minimum and maximum scores, thus passing all test cases.", "The implementation failed multiple test cases due to an IndexError. The issue lies in the for loops within the dp and max_score functions, specifically in the range function's stop argument: len(weights) - k + 2. This causes the loop to go beyond the bounds of the list, resulting in a runtime error. To overcome this error, we need to adjust the stop argument in the range function to ensure that we only access elements within the bounds of the list. We can modify the loop condition to while j < len(weights) - k + 1, which will prevent the IndexError and ensure correct marble distribution calculations."], "solution": "def putMarbles(weights: List[int], k: int) -> int:\n \"\"\"\n You have k bags. You are given a 0-indexed integer array weights where weights[i] is the weight of the ith marble. You are also given the integer k.\n Divide the marbles into the k bags according to the following rules:\n No bag is empty.\n If the ith marble and jth marble are in a bag, then all marbles with an index between the ith and jth indices should also be in that same bag.\n If a bag consists of all the marbles with an index from i to j inclusively, then the cost of the bag is weights[i] + weights[j].\n The score after distributing the marbles is the sum of the costs of all the k bags.\n Return the difference between the maximum and minimum scores among marble distributions.\n Constraints:\n \n 1 <= k <= weights.length <= 105\n 1 <= weights[i] <= 109\n \"\"\"\n def dp(i: int, k: int) -> int:\n if (i, k) in memo:\n return memo[(i, k)]\n if k == 1:\n memo[(i, k)] = weights[i] + weights[-1]\n return memo[(i, k)]\n min_score = float('inf')\n for j in range(i, len(weights) - k + 1):\n min_score = min(min_score, weights[i] + weights[j] + dp(j + 1, k - 1))\n memo[(i, k)] = min_score\n return memo[(i, k)]\n\n def max_score(i: int, k: int) -> int:\n if (i, k) in max_memo:\n return max_memo[(i, k)]\n if k == 1:\n max_memo[(i, k)] = weights[i] + weights[-1]\n return max_memo[(i, k)]\n max_current_score = float('-inf')\n for j in range(i, len(weights) - k + 1):\n max_current_score = max(max_current_score, weights[i] + weights[j] + max_score(j + 1, k - 1))\n max_memo[(i, k)] = max_current_score\n return max_memo[(i, k)]\n\n memo = {}\n max_memo = {}\n return max_score(0, k) - dp(0, k)"} +{"task_id": "shortest-cycle-in-a-graph", "prompt": "def findShortestCycle(n: int, edges: List[List[int]]) -> int:\n \"\"\"\n There is a bi-directional graph with n vertices, where each vertex is labeled from 0 to n - 1. The edges in the graph are represented by a given 2D integer array edges, where edges[i] = [ui, vi] denotes an edge between vertex ui and vertex vi. Every vertex pair is connected by at most one edge, and no vertex has an edge to itself.\n Return the length of the shortest cycle in the graph. If no cycle exists, return -1.\n A cycle is a path that starts and ends at the same node, and each edge in the path is used only once.\n Constraints:\n \n 2 <= n <= 1000\n 1 <= edges.length <= 1000\n edges[i].length == 2\n 0 <= ui, vi < n\n ui != vi\n There are no repeated edges.\n \"\"\"\n", "entry_point": "shortest-cycle-in-a-graph", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases, returning incorrect cycle lengths. The issue lies in the fact that the algorithm does not correctly identify cycles, as it considers a visited node as part of a cycle without checking if it has traversed enough edges to form a valid cycle. This leads to the algorithm returning a cycle length shorter than the actual shortest cycle. To overcome this error, we need to modify the algorithm to ensure that a cycle is only considered when the path has traversed at least 3 edges. This can be done by adding a condition to check if the depth is greater than or equal to 2 before updating the shortest_cycle variable.", "The implementation failed 3 out of the 5 test cases, returning incorrect cycle lengths. The issue lies in the way the algorithm detects cycles. When a visited node is encountered, it assumes a cycle is found and updates the shortest_cycle variable. However, this approach does not account for the fact that the visited node might be part of a different cycle or a non-cycle path. To overcome this error, we need to modify the algorithm to ensure that it only considers valid cycles when updating the shortest_cycle variable. One possible solution is to use a depth-first search approach and track the visited nodes in a separate data structure, ensuring that we only update the shortest_cycle variable when a valid cycle is detected.", "The implementation failed 3 out of the 5 test cases, returning incorrect cycle lengths. The issue lies in the dfs function, where it calculates the shortest cycle without considering the depth of the parent node. This results in an incorrect cycle length calculation. To overcome this error, we need to account for the parent's depth when updating the shortest_cycle variable. We can do this by modifying the line shortest_cycle = min(shortest_cycle, depth + 1) to shortest_cycle = min(shortest_cycle, depth - visited[parent] + 1). This change will ensure that the cycle length calculation takes into account the parent's depth, thus providing the correct cycle length.", "The implementation failed 3 out of the 5 test cases, producing incorrect cycle lengths or returning -1 when a cycle exists. The issue lies in the dfs function, specifically in the calculation of the shortest_cycle variable. The current calculation uses depth - visited[parent] + 1, which does not correctly account for the cycle length. Instead, we should use depth - visited[neighbor] + 1 to correctly calculate the cycle length. By making this change, the implementation will correctly identify the shortest cycle in the graph and return the correct cycle length."], "solution": "def findShortestCycle(n: int, edges: List[List[int]]) -> int:\n \"\"\"\n There is a bi-directional graph with n vertices, where each vertex is labeled from 0 to n - 1. The edges in the graph are represented by a given 2D integer array edges, where edges[i] = [ui, vi] denotes an edge between vertex ui and vertex vi. Every vertex pair is connected by at most one edge, and no vertex has an edge to itself.\n Return the length of the shortest cycle in the graph. If no cycle exists, return -1.\n A cycle is a path that starts and ends at the same node, and each edge in the path is used only once.\n Constraints:\n \n 2 <= n <= 1000\n 1 <= edges.length <= 1000\n edges[i].length == 2\n 0 <= ui, vi < n\n ui != vi\n There are no repeated edges.\n \"\"\"\n from collections import defaultdict\n\n def dfs(node, parent, depth):\n nonlocal shortest_cycle\n visited[node] = depth\n for neighbor in graph[node]:\n if not visited[neighbor]:\n dfs(neighbor, node, depth + 1)\n elif neighbor != parent:\n shortest_cycle = min(shortest_cycle, depth - visited[neighbor] + 1)\n\n graph = defaultdict(list)\n for u, v in edges:\n graph[u].append(v)\n graph[v].append(u)\n\n shortest_cycle = float('inf')\n for start in range(n):\n visited = [0] * n\n dfs(start, -1, 1)\n\n return shortest_cycle if shortest_cycle != float('inf') else -1"} +{"task_id": "minimum-time-to-complete-all-tasks", "prompt": "def findMinimumTime(tasks: List[List[int]]) -> int:\n \"\"\"\n There is a computer that can run an unlimited number of tasks at the same time. You are given a 2D integer array tasks where tasks[i] = [starti, endi, durationi] indicates that the ith task should run for a total of durationi seconds (not necessarily continuous) within the inclusive time range [starti, endi].\n You may turn on the computer only when it needs to run a task. You can also turn it off if it is idle.\n Return the minimum time during which the computer should be turned on to complete all tasks.\n Constraints:\n \n 1 <= tasks.length <= 2000\n tasks[i].length == 3\n 1 <= starti, endi <= 2000\n 1 <= durationi <= endi - starti + 1\n \"\"\"\n", "entry_point": "minimum-time-to-complete-all-tasks", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, producing incorrect results for each case. The issue lies in the calculation of the minimum time the computer should be turned on. The current implementation calculates the sum of all the dp values and subtracts the maximum dp value, which does not accurately represent the minimum time the computer should be turned on. Instead, we should be looking for the maximum total duration that can be achieved within the given time ranges and then subtract that from the sum of all task durations. To fix this issue, we need to modify the logic to calculate the maximum total duration and then subtract it from the sum of all task durations to get the minimum time the computer should be turned on.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or 0 as output. The issue lies in the dynamic programming approach used to calculate the maximum total duration. The current implementation sorts the tasks based on their end times and iterates through them, updating the dp array. However, this approach does not account for the overlapping tasks and their respective durations. To overcome this error, we need to modify the dynamic programming approach to consider the overlapping tasks and their durations, ensuring that the computer is turned on for the minimum time required to complete all tasks. This can be done by sorting the tasks based on their start times and updating the dp array accordingly, while also considering the overlapping tasks and their durations.", "The implementation failed all the test cases except the base case. The issue lies in the logic of the dynamic programming approach used to calculate the minimum time during which the computer should be turned on. The current implementation sorts the tasks based on their start time and then iterates through the tasks, updating the dp array. However, this approach does not account for the overlapping tasks and their durations correctly, leading to incorrect results. To overcome this error, we need to revise the dynamic programming approach to handle overlapping tasks and their durations more accurately, ensuring that the minimum time during which the computer should be turned on is calculated correctly.", "The implementation failed 3 out of the 5 test cases, producing incorrect results for cases where tasks have overlapping time ranges. The issue lies in the way the dynamic programming (dp) array is being updated. The current implementation only updates the dp[end] value, which does not account for the overlapping tasks properly. To overcome this error, we need to update the dp array in a way that considers the overlapping tasks and their durations. We can achieve this by iterating through the dp array in reverse order and updating the dp values based on the minimum duration found for each task. This change will ensure that the overlapping tasks are considered, and the minimum total duration is calculated correctly."], "solution": "def findMinimumTime(tasks: List[List[int]]) -> int:\n \"\"\"\n There is a computer that can run an unlimited number of tasks at the same time. You are given a 2D integer array tasks where tasks[i] = [starti, endi, durationi] indicates that the ith task should run for a total of durationi seconds (not necessarily continuous) within the inclusive time range [starti, endi].\n You may turn on the computer only when it needs to run a task. You can also turn it off if it is idle.\n Return the minimum time during which the computer should be turned on to complete all tasks.\n Constraints:\n \n 1 <= tasks.length <= 2000\n tasks[i].length == 3\n 1 <= starti, endi <= 2000\n 1 <= durationi <= endi - starti + 1\n \"\"\"\n tasks.sort(key=lambda x: (x[1], x[0]))\n dp = [0] * (2001)\n total_duration = sum(task[2] for task in tasks)\n\n for start, end, duration in tasks:\n for t in range(end, start - 1, -1):\n dp[t] = max(dp[t], dp[start - 1] + duration)\n\n min_total_duration = float('inf')\n for i in range(2001):\n min_total_duration = min(min_total_duration, total_duration - dp[i])\n\n return min_total_duration"} +{"task_id": "count-number-of-possible-root-nodes", "prompt": "def rootCount(edges: List[List[int]], guesses: List[List[int]], k: int) -> int:\n \"\"\"\n Alice has an undirected tree with n nodes labeled from 0 to n - 1. The tree is represented as a 2D integer array edges of length n - 1 where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree.\n Alice wants Bob to find the root of the tree. She allows Bob to make several guesses about her tree. In one guess, he does the following:\n Chooses two distinct integers u and v such that there exists an edge [u, v] in the tree.\n He tells Alice that u is the parent of v in the tree.\n Bob's guesses are represented by a 2D integer array guesses where guesses[j] = [uj, vj] indicates Bob guessed uj to be the parent of vj.\n Alice being lazy, does not reply to each of Bob's guesses, but just says that at least k of his guesses are true.\n Given the 2D integer arrays edges, guesses and the integer k, return the number of possible nodes that can be the root of Alice's tree. If there is no such tree, return 0.\n Constraints:\n \n edges.length == n - 1\n 2 <= n <= 105\n 1 <= guesses.length <= 105\n 0 <= ai, bi, uj, vj <= n - 1\n ai != bi\n uj != vj\n edges represents a valid tree.\n guesses[j] is an edge of the tree.\n guesses is unique.\n 0 <= k <= guesses.length\n \"\"\"\n", "entry_point": "count-number-of-possible-root-nodes", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the dfs function, which only considers nodes in the possible_roots set when the depth is equal to k. This approach does not account for the fact that a node can be a valid root even if it is not part of the k correct guesses. To overcome this error, we need to modify the dfs function to consider all nodes as potential roots, regardless of their depth or presence in the possible_roots set. Additionally, we should update the possible_roots set to include all nodes and then remove nodes based on the number of correct guesses. This will ensure that we consider all possible root nodes and accurately count the number of valid roots.", "The implementation failed all the test cases due to a TypeError. The issue is caused by the line \"count += dfs(child, node, correct_guesses + (node, child) in guesses_set)\" where we are trying to add a boolean value to an integer. The correct approach should be to increment the correct_guesses value by 1 if the tuple (node, child) is in the guesses_set. To fix this error, we can change the line to \"count += dfs(child, node, correct_guesses + int((node, child) in guesses_set))\". This change will ensure that we increment the correct_guesses value by 1 only when the tuple (node, child) is in the guesses_set, thus avoiding the TypeError.", "The implementation failed 4 out of the 5 test cases, indicating that the logic for counting the possible roots is incorrect. The issue lies in the dfs function, which does not properly account for the number of correct guesses when traversing the tree. The current implementation adds the count of correct guesses for each child node, which can lead to overcounting the number of correct guesses. To overcome this error, we need to modify the dfs function to correctly account for the number of correct guesses and ensure that the count is not overestimated. Additionally, we should also consider the case when the number of correct guesses is greater than k, as it would not satisfy the condition given by Alice.", "The implementation failed 2 out of the 5 test cases. The issue lies in the dfs function, specifically in the condition that checks if all children of the current node are equal to the parent. This condition is not sufficient to determine if the current node is a possible root. Instead, we should check if the number of children that have the correct parent-child relationship is equal to k. To overcome this error, we need to modify the dfs function to keep track of the number of correct parent-child relationships and update the condition accordingly. This change will ensure that we correctly identify possible roots based on the given constraints."], "solution": "def rootCount(edges: List[List[int]], guesses: List[List[int]], k: int) -> int:\n \"\"\"\n Alice has an undirected tree with n nodes labeled from 0 to n - 1. The tree is represented as a 2D integer array edges of length n - 1 where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree.\n Alice wants Bob to find the root of the tree. She allows Bob to make several guesses about her tree. In one guess, he does the following:\n Chooses two distinct integers u and v such that there exists an edge [u, v] in the tree.\n He tells Alice that u is the parent of v in the tree.\n Bob's guesses are represented by a 2D integer array guesses where guesses[j] = [uj, vj] indicates Bob guessed uj to be the parent of vj.\n Alice being lazy, does not reply to each of Bob's guesses, but just says that at least k of his guesses are true.\n Given the 2D integer arrays edges, guesses and the integer k, return the number of possible nodes that can be the root of Alice's tree. If there is no such tree, return 0.\n Constraints:\n \n edges.length == n - 1\n 2 <= n <= 105\n 1 <= guesses.length <= 105\n 0 <= ai, bi, uj, vj <= n - 1\n ai != bi\n uj != vj\n edges represents a valid tree.\n guesses[j] is an edge of the tree.\n guesses is unique.\n 0 <= k <= guesses.length\n \"\"\"\n from collections import defaultdict\n\n n = len(edges) + 1\n tree = defaultdict(set)\n for u, v in edges:\n tree[u].add(v)\n tree[v].add(u)\n\n def dfs(node, parent, correct_guesses):\n if node not in tree:\n return 0\n if correct_guesses > k:\n return 0\n count = 0\n for child in tree[node]:\n if child != parent:\n count += dfs(child, node, correct_guesses + int((node, child) in guesses_set))\n if count == k:\n return 1\n return 0\n\n guesses_set = set(tuple(guess) for guess in guesses)\n possible_roots = set(range(n))\n return sum(dfs(root, -1, 0) for root in possible_roots)"} +{"task_id": "number-of-ways-to-earn-points", "prompt": "def waysToReachTarget(target: int, types: List[List[int]]) -> int:\n \"\"\"\n There is a test that has n types of questions. You are given an integer target and a 0-indexed 2D integer array types where types[i] = [counti, marksi] indicates that there are counti questions of the ith type, and each one of them is worth marksi points.\n Return the number of ways you can earn exactly target points in the exam. Since the answer may be too large, return it modulo 109 + 7.\n Note that questions of the same type are indistinguishable.\n For example, if there are 3 questions of the same type, then solving the 1st and 2nd questions is the same as solving the 1st and 3rd questions, or the 2nd and 3rd questions.\n Constraints:\n \n 1 <= target <= 1000\n n == types.length\n 1 <= n <= 50\n types[i].length == 2\n 1 <= counti, marksi <= 50\n \"\"\"\n", "entry_point": "number-of-ways-to-earn-points", "cannonical_solution": "", "test": "", "solution": "def waysToReachTarget(target: int, types: List[List[int]]) -> int:\n \"\"\"\n There is a test that has n types of questions. You are given an integer target and a 0-indexed 2D integer array types where types[i] = [counti, marksi] indicates that there are counti questions of the ith type, and each one of them is worth marksi points.\n Return the number of ways you can earn exactly target points in the exam. Since the answer may be too large, return it modulo 109 + 7.\n Note that questions of the same type are indistinguishable.\n For example, if there are 3 questions of the same type, then solving the 1st and 2nd questions is the same as solving the 1st and 3rd questions, or the 2nd and 3rd questions.\n Constraints:\n \n 1 <= target <= 1000\n n == types.length\n 1 <= n <= 50\n types[i].length == 2\n 1 <= counti, marksi <= 50\n \"\"\"\n mod = 10**9 + 7\n dp = [0] * (target + 1)\n dp[0] = 1\n\n for count, marks in types:\n for i in range(target, -1, -1):\n for j in range(1, count + 1):\n if i - j * marks >= 0:\n dp[i] = (dp[i] + dp[i - j * marks]) % mod\n else:\n break\n\n return dp[target]", "is_solved": true, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the nested loop structure, which iterates over the count of each question type. This approach does not account for the fact that questions of the same type are indistinguishable, and it overcounts the number of ways to reach the target. To fix this issue, we need to change the loop structure to correctly handle the indistinguishable questions. One possible solution is to use a different dynamic programming approach that iterates over the types of questions and updates the dp array based on the marks and count of each type, without overcounting the combinations.", "The implementation failed 4 out of the 5 test cases, producing incorrect results. The issue lies in the nested loop structure, where the inner loop iterates over the count of each type of question and updates the dp array. This approach does not account for the indistinguishability of questions of the same type, leading to overcounting the number of ways to reach the target. Additionally, the separate loop for updating dp[i] when i % marks == 0 and i // marks <= count is unnecessary and contributes to the overcounting. To fix the issue, we need to revise the algorithm to properly handle the indistinguishability of questions of the same type and avoid overcounting the number of ways to reach the target.", "The implementation failed 3 out of the 5 test cases. The issue lies in the nested loop where we iterate through the range of counts for each question type. The current implementation does not account for the combinations of different question types that can lead to the target points. Instead, it only considers the possibilities within the same question type. To overcome this error, we need to modify the algorithm to consider all possible combinations of question types and their counts that can sum up to the target points. This can be achieved by using dynamic programming to store the intermediate results and updating them based on the combinations of question types and their counts.", "The implementation failed 4 out of the 5 test cases, producing incorrect results. The issue lies in the nested for loop where the dp array is updated. The line dp[i] = (dp[i] + dp[i - marks]) % mod if i - marks >= 0 else dp[i] is redundant and causes overcounting of the ways to reach the target. This is because the previous loop with the variable j already takes into account all possible combinations of questions of the current type. To fix this error, we should remove the mentioned line, as it is not needed for the correct calculation of the number of ways to reach the target."]} +{"task_id": "split-the-array-to-make-coprime-products", "prompt": "def findValidSplit(nums: List[int]) -> int:\n \"\"\"\n You are given a 0-indexed integer array nums of length n.\n A split at an index i where 0 <= i <= n - 2 is called valid if the product of the first i + 1 elements and the product of the remaining elements are coprime.\n For example, if nums = [2, 3, 3], then a split at the index i = 0 is valid because 2 and 9 are coprime, while a split at the index i = 1 is not valid because 6 and 3 are not coprime. A split at the index i = 2 is not valid because i == n - 1.\n Return the smallest index i at which the array can be split validly or -1 if there is no such split.\n Two values val1 and val2 are coprime if gcd(val1, val2) == 1 where gcd(val1, val2) is the greatest common divisor of val1 and val2.\n Constraints:\n \n n == nums.length\n 1 <= n <= 104\n 1 <= nums[i] <= 106\n \"\"\"\n", "entry_point": "split-the-array-to-make-coprime-products", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed one test case where the output should be 0, but it returned 3. The issue in the implementation is due to the inefficient calculation of the products of the left and right subarrays in each iteration. The current approach recalculates the products from scratch in every iteration, which can lead to incorrect results when there are large numbers in the input array. To overcome this error, we should maintain a running product for both left and right subarrays and update them in each iteration. This can be done by dividing the right product by the current element and multiplying the left product by the current element in each iteration. This change will ensure that the products are calculated correctly and efficiently, thus avoiding the incorrect output.", "The implementation failed one test case where the first element of the array is not coprime with the product of the remaining elements, but a valid split still exists. The issue arises from the initial check for gcd(left_product, right_product) == 1, which returns 0 if true. This check assumes that the first element is always part of a valid split, which is not always the case. To overcome this error, we should remove this initial check and start the loop from index 0 instead of 1. This change will ensure that all possible splits are considered, and the smallest valid split index is returned correctly.", "The implementation failed one of the test cases where the expected output was 0, but the function returned 3. The issue in the implementation is due to the use of integer division (//) when updating the right_product. This can lead to incorrect results when the division is not exact, causing the gcd calculation to be incorrect. To overcome this error, we should use floating-point division (/) instead of integer division (//) when updating the right_product. This change will ensure that the right_product is calculated accurately, leading to the correct gcd calculation and valid split determination.", "The implementation failed one test case where the split should be at index 0, but the output was 3. The issue in the implementation is due to the use of floating-point division (/) instead of integer division (//) when updating the right_product. This can cause precision errors, leading to incorrect results when calculating the greatest common divisor (gcd). To overcome this error, we should replace the floating-point division with integer division when updating the right_product. This can be done by changing right_product /= nums[i] to right_product //= nums[i]. This change will ensure that we maintain integer values throughout the calculation, thus avoiding precision errors and providing the correct output."], "solution": "def findValidSplit(nums: List[int]) -> int:\n \"\"\"\n You are given a 0-indexed integer array nums of length n.\n A split at an index i where 0 <= i <= n - 2 is called valid if the product of the first i + 1 elements and the product of the remaining elements are coprime.\n For example, if nums = [2, 3, 3], then a split at the index i = 0 is valid because 2 and 9 are coprime, while a split at the index i = 1 is not valid because 6 and 3 are not coprime. A split at the index i = 2 is not valid because i == n - 1.\n Return the smallest index i at which the array can be split validly or -1 if there is no such split.\n Two values val1 and val2 are coprime if gcd(val1, val2) == 1 where gcd(val1, val2) is the greatest common divisor of val1 and val2.\n Constraints:\n \n n == nums.length\n 1 <= n <= 104\n 1 <= nums[i] <= 106\n \"\"\"\n from math import gcd\n\n left_product = 1\n right_product = 1\n for num in nums:\n right_product *= num\n\n for i in range(len(nums) - 1):\n left_product *= nums[i]\n right_product //= nums[i]\n if gcd(left_product, right_product) == 1:\n return i\n\n return -1"} +{"task_id": "time-to-cross-a-bridge", "prompt": "def findCrossingTime(n: int, k: int, time: List[List[int]]) -> int:\n \"\"\"\n There are k workers who want to move n boxes from an old warehouse to a new one. You are given the two integers n and k, and a 2D integer array time of size k x 4 where time[i] = [leftToRighti, pickOldi, rightToLefti, putNewi].\n The warehouses are separated by a river and connected by a bridge. The old warehouse is on the right bank of the river, and the new warehouse is on the left bank of the river. Initially, all k workers are waiting on the left side of the bridge. To move the boxes, the ith worker (0-indexed) can :\n Cross the bridge from the left bank (new warehouse) to the right bank (old warehouse) in leftToRighti minutes.\n Pick a box from the old warehouse and return to the bridge in pickOldi minutes. Different workers can pick up their boxes simultaneously.\n Cross the bridge from the right bank (old warehouse) to the left bank (new warehouse) in rightToLefti minutes.\n Put the box in the new warehouse and return to the bridge in putNewi minutes. Different workers can put their boxes simultaneously.\n A worker i is less efficient than a worker j if either condition is met:\n leftToRighti + rightToLefti > leftToRightj + rightToLeftj\n leftToRighti + rightToLefti == leftToRightj + rightToLeftj and i > j\n The following rules regulate the movement of the workers through the bridge :\n If a worker x reaches the bridge while another worker y is crossing the bridge, x waits at their side of the bridge.\n If the bridge is free, the worker waiting on the right side of the bridge gets to cross the bridge. If more than one worker is waiting on the right side, the one with the lowest efficiency crosses first.\n If the bridge is free and no worker is waiting on the right side, and at least one box remains at the old warehouse, the worker on the left side of the river gets to cross the bridge. If more than one worker is waiting on the left side, the one with the lowest efficiency crosses first.\n Return the instance of time at which the last worker reaches the left bank of the river after all n boxes have been put in the new warehouse.\n Constraints:\n \n 1 <= n, k <= 104\n time.length == k\n time[i].length == 4\n 1 <= leftToRighti, pickOldi, rightToLefti, putNewi <= 1000\n \"\"\"\n", "entry_point": "time-to-cross-a-bridge", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all the test cases due to a NameError. The issue is caused by the use of 'heapq' without importing it properly. The import statement 'from heapq import heappush, heappop' is used, but the code still refers to the module as 'heapq'. To fix this error, we should either change the import statement to 'import heapq' or update the code to use the imported functions 'heappush' and 'heappop' directly without referring to the 'heapq' module.", "The implementation failed all test cases due to a NameError, specifically 'heapify' is not defined. The issue stems from the fact that the 'heapify' function was not imported from the heapq module, and thus it is not recognized. To overcome this error, we need to import the 'heapify' function from the heapq module by adding 'from heapq import heapify' at the beginning of the code. This change will ensure that the 'heapify' function is properly imported and recognized, thus avoiding the NameError.", "The implementation failed 5 out of the 5 test cases, producing incorrect results for all of them. The issue lies in the calculation of total_time and the handling of workers on the left and right sides of the bridge. The current implementation does not properly account for the waiting time of workers on both sides of the bridge, and it does not correctly prioritize the workers based on their efficiency. To overcome these errors, we need to reevaluate the logic for updating total_time and handling the workers' movement across the bridge. We should ensure that the workers' waiting times are considered, and the workers are prioritized based on their efficiency as described in the problem statement.", "The implementation failed 3 out of the 5 test cases. The issue lies in the handling of the workers' efficiency and their order of crossing the bridge. The current implementation only considers the crossing time for sorting the workers, but it should also consider the worker's index when their crossing times are equal. To fix this issue, we need to modify the sorting criteria for the workers in the left_side and right_side heaps. We should include the worker's index in the tuple, so that the heaps can sort the workers based on both crossing time and index. This will ensure that the workers with lower efficiency cross the bridge first when their crossing times are equal, thus providing the correct output for the given test cases."], "solution": "def findCrossingTime(n: int, k: int, time: List[List[int]]) -> int:\n \"\"\"\n There are k workers who want to move n boxes from an old warehouse to a new one. You are given the two integers n and k, and a 2D integer array time of size k x 4 where time[i] = [leftToRighti, pickOldi, rightToLefti, putNewi].\n The warehouses are separated by a river and connected by a bridge. The old warehouse is on the right bank of the river, and the new warehouse is on the left bank of the river. Initially, all k workers are waiting on the left side of the bridge. To move the boxes, the ith worker (0-indexed) can :\n Cross the bridge from the left bank (new warehouse) to the right bank (old warehouse) in leftToRighti minutes.\n Pick a box from the old warehouse and return to the bridge in pickOldi minutes. Different workers can pick up their boxes simultaneously.\n Cross the bridge from the right bank (old warehouse) to the left bank (new warehouse) in rightToLefti minutes.\n Put the box in the new warehouse and return to the bridge in putNewi minutes. Different workers can put their boxes simultaneously.\n A worker i is less efficient than a worker j if either condition is met:\n leftToRighti + rightToLefti > leftToRightj + rightToLeftj\n leftToRighti + rightToLefti == leftToRightj + rightToLeftj and i > j\n The following rules regulate the movement of the workers through the bridge :\n If a worker x reaches the bridge while another worker y is crossing the bridge, x waits at their side of the bridge.\n If the bridge is free, the worker waiting on the right side of the bridge gets to cross the bridge. If more than one worker is waiting on the right side, the one with the lowest efficiency crosses first.\n If the bridge is free and no worker is waiting on the right side, and at least one box remains at the old warehouse, the worker on the left side of the river gets to cross the bridge. If more than one worker is waiting on the left side, the one with the lowest efficiency crosses first.\n Return the instance of time at which the last worker reaches the left bank of the river after all n boxes have been put in the new warehouse.\n Constraints:\n \n 1 <= n, k <= 104\n time.length == k\n time[i].length == 4\n 1 <= leftToRighti, pickOldi, rightToLefti, putNewi <= 1000\n \"\"\"\n from heapq import heappush, heappop, heapify\n\n left_side = [(time[i][0], time[i][0] + time[i][2], i) for i in range(k)]\n right_side = []\n heapify(left_side)\n \n total_time = 0\n boxes_moved = 0\n \n while boxes_moved < n:\n while left_side and left_side[0][0] <= total_time:\n crossing_time, efficiency, worker = heappop(left_side)\n heappush(right_side, (total_time + time[worker][1] + time[worker][2], efficiency, worker))\n \n if right_side:\n crossing_time, efficiency, worker = heappop(right_side)\n total_time = max(total_time, crossing_time)\n boxes_moved += 1\n heappush(left_side, (total_time + time[worker][3] + time[worker][0], efficiency, worker))\n else:\n total_time = left_side[0][0]\n \n return total_time"} +{"task_id": "check-if-point-is-reachable", "prompt": "def isReachable(targetX: int, targetY: int) -> bool:\n \"\"\"\n There exists an infinitely large grid. You are currently at point (1, 1), and you need to reach the point (targetX, targetY) using a finite number of steps.\n In one step, you can move from point (x, y) to any one of the following points:\n (x, y - x)\n (x - y, y)\n (2 * x, y)\n (x, 2 * y)\n Given two integers targetX and targetY representing the X-coordinate and Y-coordinate of your final position, return true if you can reach the point from (1, 1) using some number of steps, and false otherwise.\n Constraints:\n \n 1 <= targetX, targetY <= 109\n \"\"\"\n", "entry_point": "check-if-point-is-reachable", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 5 test cases, specifically the ones where the target point is reachable. The issue lies in the while loop, where the targetX and targetY values are reduced using the modulo operation. This approach does not account for all possible moves, leading to incorrect results. To overcome this error, we need to change the algorithm to explore all possible moves and determine if the target point is reachable. One possible approach is to use a depth-first search or breadth-first search algorithm to explore the grid and check if the target point can be reached.", "The implementation failed 2 out of the 5 test cases due to reaching the maximum recursion depth. The issue is caused by the use of a depth-first search (DFS) approach, which leads to a large number of recursive calls for cases with large targetX and targetY values. This results in a stack overflow error when the recursion depth exceeds the system's limit. To overcome this error, we need to consider an alternative approach, such as using breadth-first search (BFS) or dynamic programming, which can handle larger input values without causing a stack overflow. This change will help avoid the maximum recursion depth issue and improve the function's performance for large input cases.", "The implementation failed 3 out of the 5 test cases. The issue lies in the BFS approach, which is inefficient for large input values, causing the program to run for an extended period and potentially leading to a timeout error. Additionally, the implementation incorrectly returns True for the test case (6, 10), which should return False. This indicates that the BFS logic is not correctly identifying unreachable points. To overcome these issues, we need to optimize the algorithm and ensure that the logic correctly identifies unreachable points. One possible approach is to use a mathematical solution based on the properties of the given moves, which would be more efficient and accurate than the current BFS implementation.", "The implementation failed the test case where one of the coordinates is 1 and the other is a large number. The issue lies in the condition that checks if either targetX or targetY is 1. The current implementation checks if the difference between the maximum coordinate and 1 is even, which is incorrect. To fix this issue, we should change the condition to check if the other coordinate is an odd number. This can be done by replacing the condition with return (min(targetX, targetY) == 1) and (max(targetX, targetY) % 2 == 1). This change will ensure that the function returns the correct result when one of the coordinates is 1 and the other is a large number."], "solution": "def isReachable(targetX: int, targetY: int) -> bool:\n \"\"\"\n There exists an infinitely large grid. You are currently at point (1, 1), and you need to reach the point (targetX, targetY) using a finite number of steps.\n In one step, you can move from point (x, y) to any one of the following points:\n (x, y - x)\n (x - y, y)\n (2 * x, y)\n (x, 2 * y)\n Given two integers targetX and targetY representing the X-coordinate and Y-coordinate of your final position, return true if you can reach the point from (1, 1) using some number of steps, and false otherwise.\n Constraints:\n \n 1 <= targetX, targetY <= 109\n \"\"\"\n if targetX == 1 and targetY == 1:\n return True\n\n if min(targetX, targetY) == 1:\n return max(targetX, targetY) % 2 == 1\n\n while targetX != targetY:\n if targetX > targetY:\n targetX -= targetY\n else:\n targetY -= targetX\n\n return targetX == 1"} +{"task_id": "minimum-cost-to-split-an-array", "prompt": "def minCost(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an integer array nums and an integer k.\n Split the array into some number of non-empty subarrays. The cost of a split is the sum of the importance value of each subarray in the split.\n Let trimmed(subarray) be the version of the subarray where all numbers which appear only once are removed.\n For example, trimmed([3,1,2,4,3,4]) = [3,4,3,4].\n The importance value of a subarray is k + trimmed(subarray).length.\n For example, if a subarray is [1,2,3,3,3,4,4], then trimmed([1,2,3,3,3,4,4]) = [3,3,3,4,4].The importance value of this subarray will be k + 5.\n Return the minimum possible cost of a split of nums.\n A subarray is a contiguous non-empty sequence of elements within an array.\n Constraints:\n \n 1 <= nums.length <= 1000\n 0 <= nums[i] < nums.length\n 1 <= k <= 109\n \"\"\"\n", "entry_point": "minimum-cost-to-split-an-array", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all the test cases except the ones with empty input. The issue lies in the nested loops and the subarrays generation logic, which does not correctly split the array into non-empty subarrays and calculate the cost of each split. The current implementation calculates the cost for all possible subarrays within the range (i, j), which is not the intended behavior. To overcome this error, we need to revise the logic for generating subarrays and calculating the cost of each split. We should consider using dynamic programming or other optimization techniques to find the minimum cost of a split of nums while adhering to the problem constraints.", "The implementation failed 3 out of the 5 test cases. The issue lies in the calculation of the minimum cost in the dp function. The current implementation calculates the importance value for each subarray by adding k to the trimmed length, but it does not consider the cost of splitting the subarray further. As a result, the function returns a lower cost than the actual minimum cost. To overcome this error, we need to ensure that the cost of splitting the subarray is considered in the calculation of the minimum cost. This can be done by modifying the min_cost calculation in the dp function to account for the cost of splitting the subarray further.", "The implementation failed 3 out of the 5 test cases. The issue lies in the calculation of the minimum cost in the dp function. The current implementation does not account for the fact that the subarrays need to be non-empty and contiguous. As a result, the minimum cost calculated is incorrect for certain cases. To fix this issue, we need to ensure that the subarrays are non-empty and contiguous while calculating the minimum cost. This can be done by modifying the dp function to consider all possible contiguous subarrays and updating the minimum cost accordingly.", "The implementation failed 3 out of the 5 test cases, producing incorrect results for cases where the optimal split is not a single subarray. The issue lies in the dp function, which calculates the minimum cost by considering only the current subarray and the remaining elements. This approach does not account for the possibility of splitting the array into multiple subarrays to minimize the cost. To overcome this error, we need to modify the dp function to consider all possible splits of the array and calculate the minimum cost accordingly. This can be done by adding an inner loop to iterate through all possible splits and recursively calling the dp function for the remaining elements. This change will ensure that the implementation considers all possible splits and returns the correct minimum cost."], "solution": "def minCost(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an integer array nums and an integer k.\n Split the array into some number of non-empty subarrays. The cost of a split is the sum of the importance value of each subarray in the split.\n Let trimmed(subarray) be the version of the subarray where all numbers which appear only once are removed.\n For example, trimmed([3,1,2,4,3,4]) = [3,4,3,4].\n The importance value of a subarray is k + trimmed(subarray).length.\n For example, if a subarray is [1,2,3,3,3,4,4], then trimmed([1,2,3,3,3,4,4]) = [3,3,3,4,4].The importance value of this subarray will be k + 5.\n Return the minimum possible cost of a split of nums.\n A subarray is a contiguous non-empty sequence of elements within an array.\n Constraints:\n \n 1 <= nums.length <= 1000\n 0 <= nums[i] < nums.length\n 1 <= k <= 109\n \"\"\"\n from functools import lru_cache\n\n def trimmed(subarray: List[int]) -> List[int]:\n counts = {}\n for num in subarray:\n counts[num] = counts.get(num, 0) + 1\n return [num for num in subarray if counts[num] > 1]\n\n def importance_value(subarray: List[int]) -> int:\n return k + len(trimmed(subarray))\n\n @lru_cache(None)\n def dp(i: int) -> int:\n if i == len(nums):\n return 0\n min_cost = float('inf')\n counts = {}\n trimmed_len = 0\n for j in range(i, len(nums)):\n counts[nums[j]] = counts.get(nums[j], 0) + 1\n if counts[nums[j]] == 2:\n trimmed_len += 2\n elif counts[nums[j]] > 2:\n trimmed_len += 1\n for split in range(j + 1, len(nums) + 1):\n min_cost = min(min_cost, k + trimmed_len + dp(split))\n return min_cost\n\n return dp(0)"} +{"task_id": "difference-between-maximum-and-minimum-price-sum", "prompt": "def maxOutput(n: int, edges: List[List[int]], price: List[int]) -> int:\n \"\"\"\n There exists an undirected and initially unrooted tree with n nodes indexed from 0 to n - 1. You are given the integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree.\n Each node has an associated price. You are given an integer array price, where price[i] is the price of the ith node.\n The price sum of a given path is the sum of the prices of all nodes lying on that path.\n The tree can be rooted at any node root of your choice. The incurred cost after choosing root is the difference between the maximum and minimum price sum amongst all paths starting at root.\n Return the maximum possible cost amongst all possible root choices.\n Constraints:\n \n 1 <= n <= 105\n edges.length == n - 1\n 0 <= ai, bi <= n - 1\n edges represents a valid tree.\n price.length == n\n 1 <= price[i] <= 105\n \"\"\"\n", "entry_point": "difference-between-maximum-and-minimum-price-sum", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, producing incorrect output values. The issue lies in the dfs function, which only starts the depth-first search from node 0. This approach does not consider all possible root choices, as required by the problem statement. To overcome this error, we need to modify the dfs function to start the search from every node in the tree and calculate the maximum possible cost for each root choice. We can achieve this by iterating through all nodes and calling the dfs function for each node as the starting point. Additionally, we should reset the max_path and min_path arrays before each dfs call to ensure correct calculations for each root choice. Finally, we should return the maximum cost found across all root choices.", "The implementation failed 4 out of the 5 test cases, producing incorrect results. The issue lies in the fact that the dfs function is called inside the loop for each node, causing unnecessary recalculations and leading to incorrect results. To overcome this error, we should call the dfs function only once for the initial root node and then update the max_path and min_path values iteratively for the remaining nodes. This will ensure that we calculate the correct max_cost value for each possible root choice without redundant calculations.", "The implementation failed all 5 test cases, producing incorrect output values. The issue lies in the update_paths function, where the max_path and min_path values are updated incorrectly. The current implementation adds the price of the current node to the max_path and min_path values, which leads to an overestimation of the path sums. To fix this issue, we need to revise the update_paths function to correctly update the max_path and min_path values without overestimating the path sums. Additionally, we should re-evaluate the logic used in the dfs and update_paths functions to ensure that the maximum and minimum price sums are calculated accurately for all possible root choices.", "The implementation failed 5 out of the 5 test cases, producing incorrect output values. The issue lies in the update_paths function, where the max_path and min_path values are updated incorrectly. The current implementation updates the max_path and min_path values by adding the price of the current node, which leads to an overestimation of the path sums. To fix this issue, we need to revise the update_paths function to correctly update the max_path and min_path values for each node, taking into account the path sums from the parent node and the price of the current node. This will ensure that the correct path sums are calculated, and the maximum possible cost is returned for all possible root choices."], "solution": "def maxOutput(n: int, edges: List[List[int]], price: List[int]) -> int:\n \"\"\"\n There exists an undirected and initially unrooted tree with n nodes indexed from 0 to n - 1. You are given the integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree.\n Each node has an associated price. You are given an integer array price, where price[i] is the price of the ith node.\n The price sum of a given path is the sum of the prices of all nodes lying on that path.\n The tree can be rooted at any node root of your choice. The incurred cost after choosing root is the difference between the maximum and minimum price sum amongst all paths starting at root.\n Return the maximum possible cost amongst all possible root choices.\n Constraints:\n \n 1 <= n <= 105\n edges.length == n - 1\n 0 <= ai, bi <= n - 1\n edges represents a valid tree.\n price.length == n\n 1 <= price[i] <= 105\n \"\"\"\n from collections import defaultdict\n\n def dfs(node: int, parent: int) -> None:\n for neighbor in graph[node]:\n if neighbor != parent:\n dfs(neighbor, node)\n max_path[node] = max(max_path[node], max_path[neighbor] + price[neighbor])\n min_path[node] = min(min_path[node], min_path[neighbor] + price[neighbor])\n\n def update_paths(node: int, parent: int) -> None:\n for neighbor in graph[node]:\n if neighbor != parent:\n max_path[neighbor] = max(max_path[neighbor], max_path[node] - max_path[neighbor] - price[neighbor] + price[node])\n min_path[neighbor] = min(min_path[neighbor], min_path[node] - min_path[neighbor] - price[neighbor] + price[node])\n update_paths(neighbor, node)\n\n graph = defaultdict(list)\n for a, b in edges:\n graph[a].append(b)\n graph[b].append(a)\n\n max_path = [0] * n\n min_path = [0] * n\n dfs(0, -1)\n update_paths(0, -1)\n\n max_cost = 0\n for i in range(n):\n max_cost = max(max_cost, max_path[i] - min_path[i])\n\n return max_cost"} +{"task_id": "maximize-the-minimum-powered-city", "prompt": "def maxPower(stations: List[int], r: int, k: int) -> int:\n \"\"\"\n You are given a 0-indexed integer array stations of length n, where stations[i] represents the number of power stations in the ith city.\n Each power station can provide power to every city in a fixed range. In other words, if the range is denoted by r, then a power station at city i can provide power to all cities j such that |i - j| <= r and 0 <= i, j <= n - 1.\n Note that |x| denotes absolute value. For example, |7 - 5| = 2 and |3 - 10| = 7.\n The power of a city is the total number of power stations it is being provided power from.\n The government has sanctioned building k more power stations, each of which can be built in any city, and have the same range as the pre-existing ones.\n Given the two integers r and k, return the maximum possible minimum power of a city, if the additional power stations are built optimally.\n Note that you can build the k power stations in multiple cities.\n Constraints:\n \n n == stations.length\n 1 <= n <= 105\n 0 <= stations[i] <= 105\n 0 <= r <= n - 1\n 0 <= k <= 109\n \"\"\"\n", "entry_point": "maximize-the-minimum-powered-city", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the binary search logic and the power function. The power function calculates the power of a city incorrectly, leading to an incorrect count of the additional power stations needed. Additionally, the binary search logic does not correctly update the left and right pointers, causing the search to converge on an incorrect value. To fix these issues, we need to revise the power function to accurately calculate the power of a city and adjust the binary search logic to correctly update the left and right pointers based on the count of additional power stations needed.", "The implementation failed 3 out of the 5 test cases, specifically those where the range r is greater than 0. The issue lies in the binary search logic, which doesn't account for the optimal placement of the additional power stations. The current implementation only checks if the power of a city is less than the mid value and adds the difference to the count. However, this approach doesn't ensure that the additional power stations are built optimally to maximize the minimum power of a city. To overcome this error, we need to modify the binary search logic to consider the optimal placement of the additional power stations, taking into account the range r and the number of power stations k.", "The implementation failed 3 out of the 5 test cases. The issue lies in the can_reach_min_power function, which does not correctly account for the optimal placement of the additional power stations. The current approach increments the index i by 2 * r + 1 when a city's power is less than the minimum power, which may not be the optimal way to place the additional power stations. To overcome this error, we need to revise the can_reach_min_power function to better handle the optimal placement of the additional power stations, ensuring that the minimum power requirement is met while not exceeding the allowed number of additional power stations.", "The implementation failed 3 out of the 5 test cases, specifically those where the range r is greater than 0. The issue lies in the calculation of the number of stations needed in the can_reach_min_power function. The current calculation does not take into account the overlapping coverage of power stations when the range is greater than 0. This leads to an underestimation of the power provided by the additional stations, causing the function to return a lower value than expected. To overcome this error, we need to adjust the calculation of stations_needed to account for the overlapping coverage of power stations when the range is greater than 0. This can be done by considering the total coverage provided by each additional station and updating the calculation accordingly."], "solution": "def maxPower(stations: List[int], r: int, k: int) -> int:\n \"\"\"\n You are given a 0-indexed integer array stations of length n, where stations[i] represents the number of power stations in the ith city.\n Each power station can provide power to every city in a fixed range. In other words, if the range is denoted by r, then a power station at city i can provide power to all cities j such that |i - j| <= r and 0 <= i, j <= n - 1.\n Note that |x| denotes absolute value. For example, |7 - 5| = 2 and |3 - 10| = 7.\n The power of a city is the total number of power stations it is being provided power from.\n The government has sanctioned building k more power stations, each of which can be built in any city, and have the same range as the pre-existing ones.\n Given the two integers r and k, return the maximum possible minimum power of a city, if the additional power stations are built optimally.\n Note that you can build the k power stations in multiple cities.\n Constraints:\n \n n == stations.length\n 1 <= n <= 105\n 0 <= stations[i] <= 105\n 0 <= r <= n - 1\n 0 <= k <= 109\n \"\"\"\n n = len(stations)\n prefix_sum = [0] * (n + 1)\n\n for i in range(n):\n prefix_sum[i + 1] = prefix_sum[i] + stations[i]\n\n def power(i: int) -> int:\n return prefix_sum[min(i + r + 1, n)] - prefix_sum[max(i - r, 0)]\n\n def can_reach_min_power(min_power: int) -> bool:\n required_stations = 0\n i = 0\n while i < n:\n if power(i) < min_power:\n deficit = min_power - power(i)\n stations_needed = -(-deficit // (r + 1)) # ceil division\n required_stations += stations_needed\n i += stations_needed * (r + 1)\n else:\n i += 1\n if required_stations > k:\n return False\n return True\n\n left, right = 0, k + 1\n while left < right:\n mid = (left + right) // 2\n if can_reach_min_power(mid):\n left = mid + 1\n else:\n right = mid\n\n return left - 1"} +{"task_id": "count-anagrams", "prompt": "def countAnagrams(s: str) -> int:\n \"\"\"\n You are given a string s containing one or more words. Every consecutive pair of words is separated by a single space ' '.\n A string t is an anagram of string s if the ith word of t is a permutation of the ith word of s.\n For example, \"acb dfe\" is an anagram of \"abc def\", but \"def cab\" and \"adc bef\" are not.\n Return the number of distinct anagrams of s. Since the answer may be very large, return it modulo 109 + 7.\n Constraints:\n \n 1 <= s.length <= 105\n s consists of lowercase English letters and spaces ' '.\n There is single space between consecutive words.\n \"\"\"\n", "entry_point": "count-anagrams", "cannonical_solution": "", "test": "", "solution": "def countAnagrams(s: str) -> int:\n \"\"\"\n You are given a string s containing one or more words. Every consecutive pair of words is separated by a single space ' '.\n A string t is an anagram of string s if the ith word of t is a permutation of the ith word of s.\n For example, \"acb dfe\" is an anagram of \"abc def\", but \"def cab\" and \"adc bef\" are not.\n Return the number of distinct anagrams of s. Since the answer may be very large, return it modulo 109 + 7.\n Constraints:\n \n 1 <= s.length <= 105\n s consists of lowercase English letters and spaces ' '.\n There is single space between consecutive words.\n \"\"\"\n from collections import Counter\n from math import factorial\n from functools import reduce\n import operator\n\n MOD = 10**9 + 7\n\n def count_permutations(s: str) -> int:\n counter = Counter(s)\n total_permutations = factorial(len(s)) // reduce(operator.mul, (factorial(v) for v in counter.values()), 1)\n return total_permutations % MOD\n\n words = s.split(\" \")\n total_anagrams = 1\n for word in words:\n total_anagrams = (total_anagrams * count_permutations(word)) % MOD\n\n return total_anagrams", "is_solved": true, "reflections": ["The implementation failed 4 out of the 5 test cases, producing incorrect results for the number of distinct anagrams. The issue lies in the count_permutations function, which calculates the number of permutations for each word individually. However, the problem statement requires us to find the number of distinct anagrams for the entire string, not just individual words. To overcome this error, we need to modify the implementation to consider the entire string as a single entity and calculate the number of distinct anagrams accordingly. This can be done by concatenating all the words in the string and then applying the count_permutations function on the concatenated string.", "The implementation failed 4 out of the 5 test cases, producing incorrect results for cases with multiple words. The issue lies in the fact that the function is calculating the permutations of the concatenated string instead of calculating the permutations of each word individually and then multiplying them together. To fix this error, we should split the input string into words, calculate the permutations for each word, and then multiply the results together. This can be done by modifying the count_permutations function to accept a list of words and using a loop to calculate the product of the permutations of each word.", "The implementation failed 4 out of the 5 test cases, producing incorrect results for the number of distinct anagrams. The issue lies in the count_permutations function, which calculates the number of permutations for each word individually and multiplies them together. This approach does not account for the fact that the anagram count should be based on the entire string, not just individual words. To fix this error, we need to modify the count_permutations function to consider the entire string as a single entity and calculate the number of distinct anagrams accordingly. This can be done by combining all the words into a single string and then calculating the permutations based on the character frequencies in the combined string.", "The implementation failed 4 out of the 5 test cases, producing incorrect results for the number of distinct anagrams. The issue lies in the count_permutations function, which calculates the total permutations of the combined string without considering the word boundaries. This leads to an overestimation of the number of anagrams, as it treats the entire input string as a single word. To fix this error, we need to split the input string into words and calculate the permutations for each word separately. Then, we can multiply the permutations of each word to get the total number of distinct anagrams. This can be done by modifying the count_permutations function to handle individual words and updating the return statement to iterate over the words in the input string."]} +{"task_id": "number-of-great-partitions", "prompt": "def countPartitions(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an array nums consisting of positive integers and an integer k.\n Partition the array into two ordered groups such that each element is in exactly one group. A partition is called great if the sum of elements of each group is greater than or equal to k.\n Return the number of distinct great partitions. Since the answer may be too large, return it modulo 109 + 7.\n Two partitions are considered distinct if some element nums[i] is in different groups in the two partitions.\n Constraints:\n \n 1 <= nums.length, k <= 1000\n 1 <= nums[i] <= 109\n \"\"\"\n", "entry_point": "number-of-great-partitions", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, producing incorrect results. The issue lies in the use of itertools.combinations, which generates all possible combinations of the given length, but does not guarantee the order of the elements. As a result, the implementation counts some partitions multiple times, leading to an overcounting of the number of distinct great partitions. To overcome this error, we need to change the approach to ensure that we only count distinct partitions and maintain the order of the elements. A possible solution could be using a recursive function or dynamic programming to explore all possible partitions while preserving the order of the elements.", "The implementation failed 5 out of the 5 test cases, producing incorrect results. The issue lies in the helper function, which does not account for the distinctness of the partitions. The current implementation counts all possible partitions without considering whether they are distinct or not. To overcome this error, we need to modify the helper function to only count distinct partitions. One way to achieve this is by keeping track of the last index added to each group and ensuring that the same element is not added to both groups in different partitions. By doing so, we can ensure that only distinct partitions are counted, thus producing the correct results.", "The implementation failed multiple test cases, indicating that the logic for counting distinct great partitions is incorrect. The issue lies in the helper function, specifically in the conditions for updating the count. The current implementation checks if the last element added to the group is different from the current element, but this does not guarantee distinct partitions. To fix this issue, we need to revise the logic for counting distinct great partitions, ensuring that we only count partitions where some element nums[i] is in different groups in the two partitions. This may require a different approach to the problem, such as using dynamic programming or memoization to efficiently count distinct partitions.", "The implementation failed 5 out of the test cases, producing incorrect results. The issue lies in the logic for counting the number of great partitions. The current implementation only considers partitions where both groups have a sum exactly equal to k. However, the problem statement requires that the sum of elements in each group should be greater than or equal to k. To fix this error, we need to modify the loop that counts the great partitions to include all partitions where the sum of elements in both groups is greater than or equal to k. This can be done by changing the range of j and l in the counting loop to range(k + 1) instead of range(k, k + 1). This change will ensure that we count all the valid great partitions as per the problem statement."], "solution": "def countPartitions(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an array nums consisting of positive integers and an integer k.\n Partition the array into two ordered groups such that each element is in exactly one group. A partition is called great if the sum of elements of each group is greater than or equal to k.\n Return the number of distinct great partitions. Since the answer may be too large, return it modulo 109 + 7.\n Two partitions are considered distinct if some element nums[i] is in different groups in the two partitions.\n Constraints:\n \n 1 <= nums.length, k <= 1000\n 1 <= nums[i] <= 109\n \"\"\"\n n = len(nums)\n MOD = 10**9 + 7\n\n # dp[i][j][l] represents the number of ways to partition the first i elements\n # into two groups, with the first group having a sum of j and the second group\n # having a sum of l.\n dp = [[[0] * (k + 1) for _ in range(k + 1)] for _ in range(n + 1)]\n dp[0][0][0] = 1\n\n for i in range(n):\n for j in range(k + 1):\n for l in range(k + 1):\n # Case 1: Add nums[i] to the first group\n if j + nums[i] <= k:\n dp[i + 1][j + nums[i]][l] += dp[i][j][l]\n dp[i + 1][j + nums[i]][l] %= MOD\n\n # Case 2: Add nums[i] to the second group\n if l + nums[i] <= k:\n dp[i + 1][j][l + nums[i]] += dp[i][j][l]\n dp[i + 1][j][l + nums[i]] %= MOD\n\n # Case 3: Do not add nums[i] to any group\n dp[i + 1][j][l] += dp[i][j][l]\n dp[i + 1][j][l] %= MOD\n\n count = 0\n for j in range(k + 1):\n for l in range(k + 1):\n if j >= k and l >= k:\n count += dp[n][j][l]\n count %= MOD\n\n return count"} +{"task_id": "cycle-length-queries-in-a-tree", "prompt": "def cycleLengthQueries(n: int, queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given an integer n. There is a complete binary tree with 2n - 1 nodes. The root of that tree is the node with the value 1, and every node with a value val in the range [1, 2n - 1 - 1] has two children where:\n The left node has the value 2 * val, and\n The right node has the value 2 * val + 1.\n You are also given a 2D integer array queries of length m, where queries[i] = [ai, bi]. For each query, solve the following problem:\n Add an edge between the nodes with values ai and bi.\n Find the length of the cycle in the graph.\n Remove the added edge between nodes with values ai and bi.\n Note that:\n A cycle is a path that starts and ends at the same node, and each edge in the path is visited only once.\n The length of a cycle is the number of edges visited in the cycle.\n There could be multiple edges between two nodes in the tree after adding the edge of the query.\n Return an array answer of length m where answer[i] is the answer to the ith query.\n Constraints:\n \n 2 <= n <= 30\n m == queries.length\n 1 <= m <= 105\n queries[i].length == 2\n 1 <= ai, bi <= 2n - 1\n ai != bi\n \"\"\"\n", "entry_point": "cycle-length-queries-in-a-tree", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all the test cases, producing incorrect cycle lengths for each query. The issue lies in the calculation of the cycle length. The current implementation calculates the cycle length by adding the depths of both nodes and then subtracting twice the depth of their common ancestor. However, this approach is incorrect as it does not account for the additional edge between the nodes in the query. To fix this error, we need to modify the cycle length calculation to account for the additional edge. One way to do this is to add 1 to the cycle length after finding the common ancestor of the nodes. This will ensure that the additional edge is included in the cycle length calculation, producing the correct results for each query.", "The implementation failed all the test cases, producing incorrect cycle lengths for each query. The issue lies in the calculation of the cycle length. The current implementation calculates the sum of the depths of nodes a and b, and then subtracts twice the depth of their common ancestor. However, this approach is incorrect, as it does not account for the additional edge between nodes a and b. To fix this error, we should add 2 to the cycle length calculation instead of subtracting twice the depth of the common ancestor. This will ensure that the additional edge between nodes a and b is considered in the cycle length calculation, producing the correct results for each query.", "The implementation failed multiple test cases, producing incorrect cycle lengths for the given queries. The issue lies in the calculation of the cycle_length variable, which is initialized as a_depth + b_depth + 2. This initialization leads to incorrect cycle lengths when traversing the tree. To fix this issue, we need to reevaluate the logic for calculating the cycle length. We should initialize cycle_length as 0 and increment it by 2 each time we move up the tree, ensuring that we only count the edges in the cycle. Additionally, we should ensure that the loop continues until a and b are equal, correctly accounting for all edges in the cycle. By revising the cycle_length calculation and loop conditions, we can produce accurate results for the given test cases.", "The implementation failed several test cases, producing incorrect cycle lengths for some queries. The issue lies in the while loop where a and b are updated to find their common ancestor. The loop only updates a or b based on their depths, but it doesn't account for the possibility that both nodes have the same depth but different parents. This leads to incorrect cycle lengths being calculated. To fix this issue, we need to update the loop condition to handle cases where both nodes have the same depth but different parents. We can do this by adding an additional condition in the loop to check if a_depth == b_depth and a // 2 != b // 2, and update both a and b accordingly. This change will ensure that the correct cycle lengths are calculated for all queries."], "solution": "def cycleLengthQueries(n: int, queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given an integer n. There is a complete binary tree with 2n - 1 nodes. The root of that tree is the node with the value 1, and every node with a value val in the range [1, 2n - 1 - 1] has two children where:\n The left node has the value 2 * val, and\n The right node has the value 2 * val + 1.\n You are also given a 2D integer array queries of length m, where queries[i] = [ai, bi]. For each query, solve the following problem:\n Add an edge between the nodes with values ai and bi.\n Find the length of the cycle in the graph.\n Remove the added edge between nodes with values ai and bi.\n Note that:\n A cycle is a path that starts and ends at the same node, and each edge in the path is visited only once.\n The length of a cycle is the number of edges visited in the cycle.\n There could be multiple edges between two nodes in the tree after adding the edge of the query.\n Return an array answer of length m where answer[i] is the answer to the ith query.\n Constraints:\n \n 2 <= n <= 30\n m == queries.length\n 1 <= m <= 105\n queries[i].length == 2\n 1 <= ai, bi <= 2n - 1\n ai != bi\n \"\"\"\n def find_depth(node: int) -> int:\n depth = 0\n while node > 1:\n node //= 2\n depth += 1\n return depth\n\n result = []\n for query in queries:\n a, b = query\n a_depth, b_depth = find_depth(a), find_depth(b)\n cycle_length = 0\n while a != b:\n if a_depth > b_depth:\n a //= 2\n a_depth -= 1\n elif b_depth > a_depth:\n b //= 2\n b_depth -= 1\n else: # a_depth == b_depth and a // 2 != b // 2\n a //= 2\n b //= 2\n a_depth -= 1\n b_depth -= 1\n cycle_length += 2\n cycle_length += 2\n result.append(cycle_length)\n return result"} +{"task_id": "add-edges-to-make-degrees-of-all-nodes-even", "prompt": "def isPossible(n: int, edges: List[List[int]]) -> bool:\n \"\"\"\n There is an undirected graph consisting of n nodes numbered from 1 to n. You are given the integer n and a 2D array edges where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi. The graph can be disconnected.\n You can add at most two additional edges (possibly none) to this graph so that there are no repeated edges and no self-loops.\n Return true if it is possible to make the degree of each node in the graph even, otherwise return false.\n The degree of a node is the number of edges connected to it.\n Constraints:\n \n 3 <= n <= 105\n 2 <= edges.length <= 105\n edges[i].length == 2\n 1 <= ai, bi <= n\n ai != bi\n There are no repeated edges.\n \"\"\"\n", "entry_point": "add-edges-to-make-degrees-of-all-nodes-even", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 5 test cases, specifically the ones where the expected output is False. The issue lies in the logic for determining if it's possible to make the degree of each node even by adding at most two additional edges. The current implementation checks the number of odd-degree nodes and returns True if there are 0, 2, or 4 odd-degree nodes. However, this logic is incorrect, as it doesn't account for the connectivity of the odd-degree nodes. To fix this issue, we need to modify the logic to consider the connectivity of the odd-degree nodes and ensure that adding at most two additional edges can make the degree of each node even.", "The implementation failed 2 out of the 5 test cases. The issue lies in the logic for handling cases with 4 odd-degree nodes. The current implementation checks for a path between any two odd-degree nodes, but this is not sufficient to determine if it's possible to make the degree of each node even by adding at most two additional edges. Instead, we should check if there is a path between any pair of odd-degree nodes that doesn't involve the other two odd-degree nodes. To overcome this error, we need to modify the logic for handling cases with 4 odd-degree nodes by checking for paths between odd-degree nodes that don't involve the other odd-degree nodes. This can be done by updating the DFS traversal to avoid visiting the other odd-degree nodes when searching for a path between a pair of odd-degree nodes.", "The implementation failed 2 out of the 5 test cases, specifically the ones where the expected output is False. The issue lies in the logic for handling cases with 4 odd-degree nodes. The current implementation checks for a path between any pair of odd-degree nodes that doesn't involve the other two odd-degree nodes, which is not sufficient to determine if it's possible to make the degree of each node even by adding at most two additional edges. Instead, we should check if there is a path between any pair of odd-degree nodes that doesn't involve any other odd-degree nodes. To overcome this error, we need to modify the logic for handling cases with 4 odd-degree nodes, by checking for a path between any pair of odd-degree nodes that doesn't involve any other odd-degree nodes, and updating the avoid_nodes set accordingly.", "The implementation failed 2 out of the 5 test cases, returning True when the expected output was False. The issue lies in the logic for handling cases with 2 or 4 odd-degree nodes. The implementation assumes that if there are 2 or 4 odd-degree nodes, it is always possible to make the degree of each node even by adding edges. However, this is not always true, as demonstrated by the failing test cases. The current approach does not account for the graph's structure and the possibility that adding edges might not result in even-degree nodes. To overcome this error, we need to revise the logic for handling cases with 2 or 4 odd-degree nodes, taking into account the graph's structure and ensuring that adding edges will indeed result in even-degree nodes for all nodes in the graph."], "solution": "def isPossible(n: int, edges: List[List[int]]) -> bool:\n \"\"\"\n There is an undirected graph consisting of n nodes numbered from 1 to n. You are given the integer n and a 2D array edges where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi. The graph can be disconnected.\n You can add at most two additional edges (possibly none) to this graph so that there are no repeated edges and no self-loops.\n Return true if it is possible to make the degree of each node in the graph even, otherwise return false.\n The degree of a node is the number of edges connected to it.\n Constraints:\n \n 3 <= n <= 105\n 2 <= edges.length <= 105\n edges[i].length == 2\n 1 <= ai, bi <= n\n ai != bi\n There are no repeated edges.\n \"\"\"\n from collections import defaultdict\n from typing import List\n\n degree = defaultdict(int)\n \n for edge in edges:\n degree[edge[0]] += 1\n degree[edge[1]] += 1\n \n odd_degree_nodes = [node for node in degree if degree[node] % 2 == 1]\n \n if len(odd_degree_nodes) == 0:\n return True\n elif len(odd_degree_nodes) == 2:\n # Check if there is a path between the two odd-degree nodes\n visited = set()\n stack = [odd_degree_nodes[0]]\n \n while stack:\n node = stack.pop()\n if node in visited:\n continue\n visited.add(node)\n \n for edge in edges:\n if edge[0] == node and edge[1] not in visited:\n stack.append(edge[1])\n elif edge[1] == node and edge[0] not in visited:\n stack.append(edge[0])\n \n # If there is a path between the two odd-degree nodes, return True\n if odd_degree_nodes[1] in visited:\n return True\n else:\n return False\n elif len(odd_degree_nodes) == 4:\n # Check if there is a path between any pair of odd-degree nodes that doesn't involve any other odd-degree nodes\n for i in range(3):\n for j in range(i+1, 4):\n visited = set()\n stack = [odd_degree_nodes[i]]\n avoid_nodes = set(odd_degree_nodes) - {odd_degree_nodes[i], odd_degree_nodes[j]}\n \n while stack:\n node = stack.pop()\n if node in visited or node in avoid_nodes:\n continue\n visited.add(node)\n \n for edge in edges:\n if edge[0] == node and edge[1] not in visited and edge[1] not in avoid_nodes:\n stack.append(edge[1])\n elif edge[1] == node and edge[0] not in visited and edge[0] not in avoid_nodes:\n stack.append(edge[0])\n \n # If there is a path between the pair of odd-degree nodes that doesn't involve any other odd-degree nodes, return True\n if odd_degree_nodes[j] in visited:\n return True\n return False\n else:\n return False"} +{"task_id": "minimum-total-cost-to-make-arrays-unequal", "prompt": "def minimumTotalCost(nums1: List[int], nums2: List[int]) -> int:\n \"\"\"\n You are given two 0-indexed integer arrays nums1 and nums2, of equal length n.\n In one operation, you can swap the values of any two indices of nums1. The cost of this operation is the sum of the indices.\n Find the minimum total cost of performing the given operation any number of times such that nums1[i] != nums2[i] for all 0 <= i <= n - 1 after performing all the operations.\n Return the minimum total cost such that nums1 and nums2 satisfy the above condition. In case it is not possible, return -1.\n Constraints:\n \n n == nums1.length == nums2.length\n 1 <= n <= 105\n 1 <= nums1[i], nums2[i] <= n\n \"\"\"\n", "entry_point": "minimum-total-cost-to-make-arrays-unequal", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 5 test cases. The issue lies in the calculation of the minimum total cost. The current implementation calculates the cost by finding the minimum index for each number in nums2 and adding it to the total cost. However, this approach does not guarantee the minimum total cost, as it does not consider the possibility of swapping elements in nums1 to achieve a lower cost. To overcome this error, we need to modify the algorithm to find the optimal swaps in nums1 that minimize the total cost while ensuring that nums1[i] != nums2[i] for all 0 <= i <= n - 1. One possible approach is to use a graph-based algorithm to find the minimum cost of swapping elements in nums1.", "The implementation failed 5 out of the 5 test cases due to incorrect output values. The issue lies in the calculation of the minimum total cost. The current approach uses a depth-first search to find cycles in the graph and then calculates the cost based on the indices of the cycle nodes. However, this approach does not guarantee the minimum total cost, as it does not consider all possible swaps that could lead to a lower cost. To overcome this error, we need to change the approach to find the minimum total cost by considering all possible swaps and their costs. This can be done by using a different algorithm, such as the Hungarian algorithm or a minimum spanning tree-based approach, to find the optimal solution.", "The implementation failed 4 out of the 5 test cases due to incorrect output and IndexError. The issue stems from the find_min_cost function, which calculates the cost based on the index_map and cycle_nodes. The current approach does not handle cases where there are multiple indices with the same value in nums1, leading to IndexError. Additionally, the cost calculation logic is flawed, as it does not consider the optimal way to swap elements to minimize the total cost. To overcome these errors, we need to revise the find_min_cost function to handle multiple indices with the same value and update the cost calculation logic to find the optimal swaps. This will ensure that the function returns the correct minimum total cost for all test cases.", "The implementation failed 4 out of the 5 test cases due to incorrect output and IndexError. The issue stems from the find_min_cost function, which calculates the minimum cost for a given cycle of nodes. The current implementation calculates the cost by summing the indices of the cycle nodes, but it does not account for the possibility of having multiple indices for the same number in nums1. Additionally, the IndexError occurs when there are no available indices for a number in nums1. To overcome these issues, we need to modify the find_min_cost function to handle cases with multiple indices for the same number and ensure that we only access valid indices in the index_map. This can be done by iterating through all possible index combinations for the cycle nodes and calculating the cost for each combination, then selecting the minimum cost among them."], "solution": "def minimumTotalCost(nums1: List[int], nums2: List[int]) -> int:\n \"\"\"\n You are given two 0-indexed integer arrays nums1 and nums2, of equal length n.\n In one operation, you can swap the values of any two indices of nums1. The cost of this operation is the sum of the indices.\n Find the minimum total cost of performing the given operation any number of times such that nums1[i] != nums2[i] for all 0 <= i <= n - 1 after performing all the operations.\n Return the minimum total cost such that nums1 and nums2 satisfy the above condition. In case it is not possible, return -1.\n Constraints:\n \n n == nums1.length == nums2.length\n 1 <= n <= 105\n 1 <= nums1[i], nums2[i] <= n\n \"\"\"\n from collections import defaultdict\n from itertools import product\n\n n = len(nums1)\n index_map = defaultdict(list)\n graph = defaultdict(set)\n\n for i in range(n):\n index_map[nums1[i]].append(i)\n if nums1[i] != nums2[i]:\n graph[nums1[i]].add(nums2[i])\n\n def dfs(node, visited, cycle_nodes):\n visited.add(node)\n cycle_nodes.append(node)\n for neighbor in graph[node]:\n if neighbor not in visited:\n dfs(neighbor, visited, cycle_nodes)\n return cycle_nodes\n\n def find_min_cost(cycle_nodes):\n min_cost = float('inf')\n index_combinations = product(*(index_map[node] for node in cycle_nodes))\n for index_combination in index_combinations:\n cost = sum(index_combination) + len(cycle_nodes) - 1\n min_cost = min(min_cost, cost)\n return min_cost\n\n min_cost = 0\n visited = set()\n\n for num in graph:\n if num not in visited:\n cycle_nodes = dfs(num, visited, [])\n if len(cycle_nodes) == 1:\n return -1\n min_cost += find_min_cost(cycle_nodes)\n\n return min_cost"} +{"task_id": "maximum-number-of-points-from-grid-queries", "prompt": "def maxPoints(grid: List[List[int]], queries: List[int]) -> List[int]:\n \"\"\"\n You are given an m x n integer matrix grid and an array queries of size k.\n Find an array answer of size k such that for each integer queries[i] you start in the top left cell of the matrix and repeat the following process:\n If queries[i] is strictly greater than the value of the current cell that you are in, then you get one point if it is your first time visiting this cell, and you can move to any adjacent cell in all 4 directions: up, down, left, and right.\n Otherwise, you do not get any points, and you end this process.\n After the process, answer[i] is the maximum number of points you can get. Note that for each query you are allowed to visit the same cell multiple times.\n Return the resulting array answer.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n k == queries.length\n 1 <= k <= 104\n 1 <= grid[i][j], queries[i] <= 106\n \"\"\"\n", "entry_point": "maximum-number-of-points-from-grid-queries", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed one of the test cases where the expected output was [0, 8, 8], but the function returned [0, 8, 9]. The issue in the implementation is that it allows visiting the same cell multiple times, even when the query value is the same. This leads to an overestimation of the points for some queries. To overcome this error, we should track the visited cells separately for each query and reset the visited set for each query. This will ensure that we only count the points for the first visit to each cell for each query, thus providing the correct output.", "The implementation failed one test case where the expected output was [0, 8, 8], but the actual output was [0, 8, 9]. The issue in the implementation is that it allows visiting the same cell multiple times, which results in an incorrect count of points. To overcome this error, we need to ensure that we only visit a cell once by checking if the cell is already visited before adding it to the queue. This can be done by adding a condition in the loop that iterates through the adjacent cells, only appending the cell to the queue if it has not been visited yet. This change will ensure that we only visit each cell once, thus providing the correct count of points.", "The implementation failed one test case where the expected output was [0, 8, 8], but the function returned [0, 8, 9]. The issue in the implementation is that it allows visiting the same cell multiple times, which results in an incorrect count of points. To overcome this error, we need to modify the BFS function to ensure that a cell is only visited once per query. We can achieve this by adding a visited set for each query and checking if a cell has been visited before appending it to the queue. This change will ensure that we only count points for unique cells visited during each query, thus providing the correct output.", "The implementation failed one test case where the expected output was [0, 8, 8], but the actual output was [0, 8, 9]. The issue in the implementation is that it allows visiting the same cell multiple times, which results in an incorrect count of points. To overcome this error, we need to ensure that we only visit each cell once. We can do this by adding a condition to check if the new coordinates (new_x, new_y) are not in the visited set before appending them to the queue. This change will ensure that we only visit each cell once, thus providing the correct count of points."], "solution": "def maxPoints(grid: List[List[int]], queries: List[int]) -> List[int]:\n \"\"\"\n You are given an m x n integer matrix grid and an array queries of size k.\n Find an array answer of size k such that for each integer queries[i] you start in the top left cell of the matrix and repeat the following process:\n If queries[i] is strictly greater than the value of the current cell that you are in, then you get one point if it is your first time visiting this cell, and you can move to any adjacent cell in all 4 directions: up, down, left, and right.\n Otherwise, you do not get any points, and you end this process.\n After the process, answer[i] is the maximum number of points you can get. Note that for each query you are allowed to visit the same cell multiple times.\n Return the resulting array answer.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n k == queries.length\n 1 <= k <= 104\n 1 <= grid[i][j], queries[i] <= 106\n \"\"\"\n from collections import deque\n\n def bfs(query: int) -> int:\n visited = set()\n queue = deque([(0, 0)])\n points = 0\n\n while queue:\n x, y = queue.popleft()\n if (x, y) not in visited and 0 <= x < len(grid) and 0 <= y < len(grid[0]) and query > grid[x][y]:\n points += 1\n visited.add((x, y))\n for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n new_x, new_y = x + dx, y + dy\n if (new_x, new_y) not in visited and 0 <= new_x < len(grid) and 0 <= new_y < len(grid[0]) and query > grid[new_x][new_y]:\n queue.append((new_x, new_y))\n\n return points\n\n return [bfs(query) for query in queries]"} +{"task_id": "divide-nodes-into-the-maximum-number-of-groups", "prompt": "def magnificentSets(n: int, edges: List[List[int]]) -> int:\n \"\"\"\n You are given a positive integer n representing the number of nodes in an undirected graph. The nodes are labeled from 1 to n.\n You are also given a 2D integer array edges, where edges[i] = [ai, bi] indicates that there is a bidirectional edge between nodes ai and bi. Notice that the given graph may be disconnected.\n Divide the nodes of the graph into m groups (1-indexed) such that:\n Each node in the graph belongs to exactly one group.\n For every pair of nodes in the graph that are connected by an edge [ai, bi], if ai belongs to the group with index x, and bi belongs to the group with index y, then |y - x| = 1.\n Return the maximum number of groups (i.e., maximum m) into which you can divide the nodes. Return -1 if it is impossible to group the nodes with the given conditions.\n Constraints:\n \n 1 <= n <= 500\n 1 <= edges.length <= 104\n edges[i].length == 2\n 1 <= ai, bi <= n\n ai != bi\n There is at most one edge between any pair of vertices.\n \"\"\"\n", "entry_point": "divide-nodes-into-the-maximum-number-of-groups", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed one test case where the expected output was 3, but the function returned 2. The issue lies in the return statement, where it checks if any node belongs to group 2 and returns 2 if true. This approach does not account for disconnected components in the graph, which can have more than two groups. To fix this issue, we need to count the number of disconnected components in the graph and return the sum of the number of components and 1. This can be done by keeping track of the number of components during the DFS traversal and updating the return statement accordingly.", "The implementation failed 2 out of the 5 test cases. The issue lies in the calculation of the maximum number of groups. The current implementation returns components + 1, which overestimates the number of groups in cases where there are no edges or when the graph is a simple path. To fix this issue, we should return the correct number of groups based on the graph structure. We can do this by returning components when there are no edges or when the graph is a simple path, and components + 1 for other cases. To determine if the graph is a simple path, we can check if the number of edges is equal to the number of nodes minus 1. This change will ensure that the correct number of groups is returned for all test cases.", "The implementation failed 2 out of the 5 test cases. The issue lies in the condition to return the number of components. The current implementation returns the number of components when there are no edges or when the number of edges is equal to n - 1. However, this condition does not cover all cases where the nodes can be divided into groups according to the problem statement. To fix this issue, we need to modify the condition to return the correct number of groups. Instead of checking for specific edge counts, we should return the number of components plus the number of connected components in the graph, as each connected component can be divided into two groups. This can be done by updating the return statement to return components + connected_components.", "The implementation failed 2 out of the 5 test cases. The issue lies in the calculation of the maximum number of groups. The current implementation increments the connected_components variable for each unvisited node, but it does not properly account for the number of components. As a result, the final return statement overestimates the number of groups by adding connected_components * 2. To fix this issue, we should update the logic for calculating the maximum number of groups. Instead of adding connected_components * 2, we should add connected_components - 1, as each connected component contributes one additional group. This change will ensure that the correct number of groups is returned for all test cases."], "solution": "def magnificentSets(n: int, edges: List[List[int]]) -> int:\n \"\"\"\n You are given a positive integer n representing the number of nodes in an undirected graph. The nodes are labeled from 1 to n.\n You are also given a 2D integer array edges, where edges[i] = [ai, bi] indicates that there is a bidirectional edge between nodes ai and bi. Notice that the given graph may be disconnected.\n Divide the nodes of the graph into m groups (1-indexed) such that:\n Each node in the graph belongs to exactly one group.\n For every pair of nodes in the graph that are connected by an edge [ai, bi], if ai belongs to the group with index x, and bi belongs to the group with index y, then |y - x| = 1.\n Return the maximum number of groups (i.e., maximum m) into which you can divide the nodes. Return -1 if it is impossible to group the nodes with the given conditions.\n Constraints:\n \n 1 <= n <= 500\n 1 <= edges.length <= 104\n edges[i].length == 2\n 1 <= ai, bi <= n\n ai != bi\n There is at most one edge between any pair of vertices.\n \"\"\"\n from collections import defaultdict\n\n def dfs(node: int, group: int) -> bool:\n if node in visited:\n return visited[node] == group\n visited[node] = group\n for neighbor in graph[node]:\n if not dfs(neighbor, 3 - group):\n return False\n return True\n\n graph = defaultdict(list)\n for edge in edges:\n graph[edge[0]].append(edge[1])\n graph[edge[1]].append(edge[0])\n\n visited = {}\n connected_components = 0\n for node in range(1, n + 1):\n if node not in visited:\n connected_components += 1\n if not dfs(node, 1):\n return -1\n\n return connected_components - 1"} +{"task_id": "count-palindromic-subsequences", "prompt": "def countPalindromes(s: str) -> int:\n \"\"\"\n Given a string of digits s, return the number of palindromic subsequences of s having length 5. Since the answer may be very large, return it modulo 109 + 7.\n Note:\n A string is palindromic if it reads the same forward and backward.\n A subsequence is a string that can be derived from another string by deleting some or no characters without changing the order of the remaining characters.\n Constraints:\n \n 1 <= s.length <= 104\n s consists of digits.\n \"\"\"\n", "entry_point": "count-palindromic-subsequences", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases, producing incorrect results for cases where there are palindromic subsequences of length 5. The issue lies in the nested loops and the condition s[i] == s[k], which only considers subsequences with matching characters at the beginning and end. This approach fails to account for all possible palindromic subsequences of length 5. To overcome this error, we need to modify the algorithm to correctly identify and count all palindromic subsequences of length 5, considering all possible combinations of characters in the string.", "The implementation failed 3 out of the 5 test cases, specifically those where there are valid palindromic subsequences of length 5. The issue lies in the logic for updating the dp table, particularly when j is equal to 1 or 5. The current implementation only updates the dp table when 2 * j is less than or equal to i, which is incorrect. Instead, we should update the dp table for j == 1 or j == 5 without any additional condition. To fix this error, we need to remove the if 2 * j <= i: condition and update the dp table for j == 1 or j == 5 directly. This change will ensure that the dp table is updated correctly, and the function will return the correct count of palindromic subsequences of length 5.", "The implementation failed 2 out of the 5 test cases, specifically when the input string has a length of 5. The issue lies in the nested loop where we iterate over the range(i - 2, -1, -1) for updating dp[i][j]. When i is less than or equal to 2, the loop does not execute, causing the dp table to not be updated correctly for strings of length 5. To overcome this error, we need to modify the loop condition to ensure that it executes even when i is less than or equal to 2. We can change the loop range to range(i - 1, -1, -1) to ensure that the loop executes for all valid values of i, thus updating the dp table correctly and avoiding the issue.", "The implementation failed 3 out of the 5 test cases, producing incorrect results for cases where the expected output is 0 or greater than 1. The issue lies in the logic used to update the dp table, specifically in the else block where it iterates through all previous indices to find matching characters. This approach leads to overcounting of palindromic subsequences, as it considers all possible combinations of characters instead of only valid subsequences. To overcome this error, we need to revise the logic for updating the dp table, ensuring that we only count valid palindromic subsequences of length 5. This may involve changing the structure of the dp table or the way we iterate through the string to avoid overcounting."], "solution": "def countPalindromes(s: str) -> int:\n \"\"\"\n Given a string of digits s, return the number of palindromic subsequences of s having length 5. Since the answer may be very large, return it modulo 109 + 7.\n Note:\n A string is palindromic if it reads the same forward and backward.\n A subsequence is a string that can be derived from another string by deleting some or no characters without changing the order of the remaining characters.\n Constraints:\n \n 1 <= s.length <= 104\n s consists of digits.\n \"\"\"\n MOD = 10**9 + 7\n n = len(s)\n dp = [[[0] * 6 for _ in range(n + 1)] for _ in range(n + 1)]\n\n for i in range(n + 1):\n for j in range(n + 1):\n dp[i][j][0] = 1\n\n for i in range(1, n + 1):\n for j in range(i, n + 1):\n for k in range(1, 6):\n dp[i][j][k] = dp[i - 1][j][k] + dp[i][j - 1][k] - dp[i - 1][j - 1][k]\n if s[i - 1] == s[j - 1]:\n dp[i][j][k] = (dp[i][j][k] + dp[i - 1][j - 1][k - 1]) % MOD\n\n return dp[n][n][5] % MOD"} +{"task_id": "count-subarrays-with-median-k", "prompt": "def countSubarrays(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an array nums of size n consisting of distinct integers from 1 to n and a positive integer k.\n Return the number of non-empty subarrays in nums that have a median equal to k.\n Note:\n The median of an array is the middle element after sorting the array in ascending order. If the array is of even length, the median is the left middle element.\n For example, the median of [2,3,1,4] is 2, and the median of [8,4,3,5,1] is 4.\n A subarray is a contiguous part of an array.\n Constraints:\n \n n == nums.length\n 1 <= n <= 105\n 1 <= nums[i], k <= n\n The integers in nums are distinct.\n \"\"\"\n", "entry_point": "count-subarrays-with-median-k", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, producing incorrect counts for the number of subarrays with the given median. The issue lies in the calculation of the count, where it multiplies (left + 1) * (right + 1) for each occurrence of k in the array. This calculation does not account for the fact that the subarrays must have k as their median, and instead, it calculates all possible subarrays containing k. To fix this error, we need to modify the count calculation to only consider subarrays where k is the median. This can be done by iterating through all possible subarray lengths containing k and checking if k is the median for each subarray.", "The implementation failed 5 out of the test cases, including one that resulted in a list index out of range error. The issue lies in the nested for loops where left_length and right_length are being used to calculate the count. The conditions inside the loops are not correctly accounting for the median calculation, leading to an overcounting of valid subarrays. Additionally, the index out of range error occurs due to improper bounds checking when accessing elements in the nums list. To fix these issues, we need to revise the logic for counting valid subarrays and ensure proper bounds checking when accessing elements in the nums list.", "The implementation failed 5 out of the 5 test cases, producing incorrect counts for the number of subarrays with the given median. The issue lies in the calculation of the count, where the code multiplies (left + 1) * (right + 1) for each occurrence of k in the array. This calculation does not account for the fact that the median should be the middle element in the subarray, and it overestimates the number of valid subarrays. To overcome this error, we need to modify the count calculation to only consider subarrays where k is the median. This can be done by iterating through all possible subarray lengths containing k and checking if k is the median for each subarray. By doing this, we can accurately count the number of subarrays with the given median.", "The implementation failed 2 out of the 5 test cases. The issue lies in the nested loops that calculate the count of subarrays with a median equal to k. The current implementation only increments the count when the left and right lengths are equal, which is incorrect. The correct condition should be when the left length is greater than or equal to the right length, as the median is the left middle element for even-length subarrays. To fix this error, we need to change the condition in the inner loop to if left_length >= right_length: instead of if left_length == right_length:. This change will ensure that the count is incremented for all valid subarrays with a median equal to k."], "solution": "def countSubarrays(nums: List[int], k: int) -> int:\n \"\"\"\n You are given an array nums of size n consisting of distinct integers from 1 to n and a positive integer k.\n Return the number of non-empty subarrays in nums that have a median equal to k.\n Note:\n The median of an array is the middle element after sorting the array in ascending order. If the array is of even length, the median is the left middle element.\n For example, the median of [2,3,1,4] is 2, and the median of [8,4,3,5,1] is 4.\n A subarray is a contiguous part of an array.\n Constraints:\n \n n == nums.length\n 1 <= n <= 105\n 1 <= nums[i], k <= n\n The integers in nums are distinct.\n \"\"\"\n count = 0\n n = len(nums)\n for i in range(n):\n if nums[i] == k:\n left = i\n right = n - i - 1\n for left_length in range(1, left + 2):\n for right_length in range(1, right + 2):\n if left_length >= right_length:\n count += 1\n return count"} +{"task_id": "number-of-beautiful-partitions", "prompt": "def beautifulPartitions(s: str, k: int, minLength: int) -> int:\n \"\"\"\n You are given a string s that consists of the digits '1' to '9' and two integers k and minLength.\n A partition of s is called beautiful if:\n s is partitioned into k non-intersecting substrings.\n Each substring has a length of at least minLength.\n Each substring starts with a prime digit and ends with a non-prime digit. Prime digits are '2', '3', '5', and '7', and the rest of the digits are non-prime.\n Return the number of beautiful partitions of s. Since the answer may be very large, return it modulo 109 + 7.\n A substring is a contiguous sequence of characters within a string.\n Constraints:\n \n 1 <= k, minLength <= s.length <= 1000\n s consists of the digits '1' to '9'.\n \"\"\"\n", "entry_point": "number-of-beautiful-partitions", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 4 out of the 5 test cases, returning 0 instead of the expected number of beautiful partitions. The issue lies in the is_beautiful function, which checks if the first character is a prime digit and the last character is a non-prime digit. However, this condition is not sufficient to ensure that the entire substring is beautiful. The function needs to check that all characters in the substring, except the last one, are prime digits, and only the last character is a non-prime digit. To fix this issue, we should modify the is_beautiful function to iterate through the substring and check each character's primality, ensuring that all characters except the last one are prime digits and the last character is a non-prime digit.", "The implementation failed 4 out of the 5 test cases, indicating that the function is not correctly counting the beautiful partitions. The issue lies in the is_beautiful() function, which checks if a substring is beautiful. The current implementation checks if all characters except the last one are prime digits and if the last character is a non-prime digit. However, it should only check if the first character is a prime digit and the last character is a non-prime digit. To fix this issue, we need to modify the is_beautiful() function to only check the first and last characters of the substring, ensuring that the first character is a prime digit and the last character is a non-prime digit.", "The implementation failed 4 out of the 5 test cases, returning 0 instead of the expected number of beautiful partitions. The issue lies in the is_beautiful function, which checks if the substring starts with a prime digit and ends with a non-prime digit. However, it does not account for the case where the substring itself is of length 1, which can be a valid partition if minLength is 1. To fix this issue, we need to modify the is_beautiful function to handle the case where the substring length is 1. We can do this by adding a condition to check if the substring length is 1 and if the character is a prime digit, then return True. This change will ensure that the function correctly identifies beautiful partitions even when the substring length is 1, thus providing the correct output for the test cases.", "The implementation failed 4 out of the 5 test cases, returning 0 instead of the expected number of beautiful partitions. The issue lies in the is_beautiful function, which checks if a substring is beautiful based on its length and the prime or non-prime nature of its first and last characters. However, this function does not account for the minLength constraint, which requires each substring to have a length of at least minLength. To fix this issue, we need to update the is_beautiful function to ensure that the substring length is at least minLength and that the first character is prime and the last character is non-prime. This can be done by removing the separate check for length 1 substrings and updating the return statement to account for the minLength constraint."], "solution": "def beautifulPartitions(s: str, k: int, minLength: int) -> int:\n \"\"\"\n You are given a string s that consists of the digits '1' to '9' and two integers k and minLength.\n A partition of s is called beautiful if:\n s is partitioned into k non-intersecting substrings.\n Each substring has a length of at least minLength.\n Each substring starts with a prime digit and ends with a non-prime digit. Prime digits are '2', '3', '5', and '7', and the rest of the digits are non-prime.\n Return the number of beautiful partitions of s. Since the answer may be very large, return it modulo 109 + 7.\n A substring is a contiguous sequence of characters within a string.\n Constraints:\n \n 1 <= k, minLength <= s.length <= 1000\n s consists of the digits '1' to '9'.\n \"\"\"\n MOD = 10**9 + 7\n prime_digits = {'2', '3', '5', '7'}\n non_prime_digits = {'1', '4', '6', '8', '9'}\n\n def is_beautiful(substring: str) -> bool:\n if len(substring) < minLength:\n return False\n return substring[0] in prime_digits and substring[-1] in non_prime_digits\n\n def count_beautiful_partitions(start: int, remaining_partitions: int) -> int:\n if remaining_partitions == 0:\n return 1 if start == len(s) else 0\n if start >= len(s):\n return 0\n\n count = 0\n for end in range(start + minLength - 1, len(s)):\n if is_beautiful(s[start:end + 1]):\n count += count_beautiful_partitions(end + 1, remaining_partitions - 1)\n count %= MOD\n return count\n\n return count_beautiful_partitions(0, k)"} +{"task_id": "split-message-based-on-limit", "prompt": "def splitMessage(message: str, limit: int) -> List[str]:\n \"\"\"\n You are given a string, message, and a positive integer, limit.\n You must split message into one or more parts based on limit. Each resulting part should have the suffix \"\", where \"b\" is to be replaced with the total number of parts and \"a\" is to be replaced with the index of the part, starting from 1 and going up to b. Additionally, the length of each resulting part (including its suffix) should be equal to limit, except for the last part whose length can be at most limit.\n The resulting parts should be formed such that when their suffixes are removed and they are all concatenated in order, they should be equal to message. Also, the result should contain as few parts as possible.\n Return the parts message would be split into as an array of strings. If it is impossible to split message as required, return an empty array.\n Constraints:\n \n 1 <= message.length <= 104\n message consists only of lowercase English letters and ' '.\n 1 <= limit <= 104\n \"\"\"\n", "entry_point": "split-message-based-on-limit", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed several test cases, indicating that the message splitting logic is incorrect. The issue lies in the calculation of chunk_size and the subsequent splitting of the message. The current implementation does not account for the fact that the length of each part, including its suffix, should be equal to the limit, except for the last part. Instead, it calculates chunk_size based on the message length and the number of chunks, which leads to incorrect splitting. To fix this issue, we need to modify the logic to ensure that each part, except for the last one, has a length equal to the limit, including its suffix. We should also ensure that the last part's length does not exceed the limit.", "The implementation failed 5 out of the test cases provided. The issue lies in the calculation of the chunk_length and the suffix. The current implementation calculates the suffix length separately and then checks if the chunk_length plus the suffix length exceeds the limit. However, this approach does not guarantee that the resulting part will have a length equal to the limit, except for the last part. To fix this issue, we should calculate the chunk_length by considering the suffix length from the beginning. We can do this by setting max_chunk_length to limit - len(suffix) and updating the chunk_length calculation accordingly. This change will ensure that the resulting parts have the correct length as per the problem statement.", "The implementation failed 5 out of the test cases, primarily due to the condition if limit < 5: return []. This condition causes the function to return an empty list for cases where the limit is less than 5, even if a valid split is possible. To overcome this error, we should remove this condition and instead focus on ensuring that the message can be split into valid parts based on the given limit. By doing so, we can handle cases where the limit is less than 5 and still produce a valid split if possible.", "The implementation failed multiple test cases, primarily due to the incorrect splitting of the message into parts. The issue lies in the loop where it iterates from limit to 0, and the calculation of the chunk size. The current implementation does not account for the space required for the suffix, leading to incorrect chunk sizes and an excessive number of parts. To overcome this error, we need to adjust the chunk size calculation to account for the space required for the suffix and ensure that the message is split into the minimum number of parts while adhering to the limit constraint."], "solution": "def splitMessage(message: str, limit: int) -> List[str]:\n \"\"\"\n You are given a string, message, and a positive integer, limit.\n You must split message into one or more parts based on limit. Each resulting part should have the suffix \"\", where \"b\" is to be replaced with the total number of parts and \"a\" is to be replaced with the index of the part, starting from 1 and going up to b. Additionally, the length of each resulting part (including its suffix) should be equal to limit, except for the last part whose length can be at most limit.\n The resulting parts should be formed such that when their suffixes are removed and they are all concatenated in order, they should be equal to message. Also, the result should contain as few parts as possible.\n Return the parts message would be split into as an array of strings. If it is impossible to split message as required, return an empty array.\n Constraints:\n \n 1 <= message.length <= 104\n message consists only of lowercase English letters and ' '.\n 1 <= limit <= 104\n \"\"\"\n from typing import List\n\n message_length = len(message)\n result = []\n index = 0\n\n while index < message_length:\n max_chunk_size = limit - len(f\"<{len(result) + 1}/x>\")\n if max_chunk_size <= 0:\n return []\n chunk = message[index:index + max_chunk_size]\n result.append(chunk + f\"<{len(result) + 1}/x>\")\n index += len(chunk)\n\n for i in range(len(result)):\n result[i] = result[i].replace(\"x\", str(len(result)))\n\n return result"} +{"task_id": "maximum-number-of-non-overlapping-palindrome-substrings", "prompt": "def maxPalindromes(s: str, k: int) -> int:\n \"\"\"\n You are given a string s and a positive integer k.\n Select a set of non-overlapping substrings from the string s that satisfy the following conditions:\n The length of each substring is at least k.\n Each substring is a palindrome.\n Return the maximum number of substrings in an optimal selection.\n A substring is a contiguous sequence of characters within a string.\n Constraints:\n \n 1 <= k <= s.length <= 2000\n s consists of lowercase English letters.\n \"\"\"\n", "entry_point": "maximum-number-of-non-overlapping-palindrome-substrings", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all the test cases except the first one. The issue lies in the count_palindromes function, where it only considers the case when a palindrome is found and doesn't account for the possibility of skipping the current character and moving forward. This leads to an incorrect count of maximum palindromes. To overcome this error, we need to add another case in the count_palindromes function to consider skipping the current character and moving forward. We can do this by adding a line max_count = max(max_count, count_palindromes(start + 1, end, memo)) before updating the memo dictionary. This change will ensure that we explore all possible combinations of substrings and return the correct count of maximum palindromes.", "The implementation failed all 5 test cases, producing incorrect results. The issue lies in the count_palindromes function, where it calculates the maximum number of palindromes. The current implementation only considers the case where a palindrome starts at the 'start' index, and it doesn't explore other possibilities of selecting palindromes within the given range. This leads to suboptimal selections and incorrect results. To overcome this error, we need to modify the count_palindromes function to explore all possible selections of palindromes within the given range, ensuring that we find the optimal selection that maximizes the number of palindromes.", "The implementation failed 5 out of the 5 test cases, consistently returning 1 instead of the expected maximum number of palindromic substrings. The issue lies in the for loop within the count_palindromes function, where the range is set to start from 'start' and end at 'end - 1'. This causes the function to only consider substrings that end before the last character of the given range, leading to incorrect results. To overcome this error, we need to modify the range of the for loop to include the last character of the given range. We can do this by changing the range to start from 'start' and end at 'end'. This change will ensure that all possible substrings within the given range are considered, thus providing the correct maximum number of palindromic substrings.", "The implementation failed all the test cases except the base case. The issue lies in the logic of the count_palindromes function, specifically in the for loop where it iterates from start to end. The loop should only iterate through substrings of length at least k, but it currently iterates through all possible substrings. This results in the function returning a higher count of palindromes than expected. To fix this issue, we need to modify the loop to only consider substrings with a length of at least k. We can do this by changing the range of the loop to range(start, end - k + 2) and updating the loop body accordingly. This change will ensure that we only count palindromes with a length of at least k, thus providing the correct output."], "solution": "def maxPalindromes(s: str, k: int) -> int:\n \"\"\"\n You are given a string s and a positive integer k.\n Select a set of non-overlapping substrings from the string s that satisfy the following conditions:\n The length of each substring is at least k.\n Each substring is a palindrome.\n Return the maximum number of substrings in an optimal selection.\n A substring is a contiguous sequence of characters within a string.\n Constraints:\n \n 1 <= k <= s.length <= 2000\n s consists of lowercase English letters.\n \"\"\"\n def is_palindrome(substring: str) -> bool:\n return substring == substring[::-1]\n\n def count_palindromes(start: int, end: int, memo: dict) -> int:\n if start >= end:\n return 0\n if (start, end) in memo:\n return memo[(start, end)]\n\n max_count = 0\n for i in range(start, end - k + 2):\n if is_palindrome(s[start:i + k]):\n max_count = max(max_count, 1 + count_palindromes(i + k, end, memo))\n\n max_count = max(max_count, count_palindromes(start + 1, end, memo))\n memo[(start, end)] = max_count\n return max_count\n\n return count_palindromes(0, len(s) - 1, {})"} +{"task_id": "minimum-total-distance-traveled", "prompt": "def minimumTotalDistance(robot: List[int], factory: List[List[int]]) -> int:\n \"\"\"\n There are some robots and factories on the X-axis. You are given an integer array robot where robot[i] is the position of the ith robot. You are also given a 2D integer array factory where factory[j] = [positionj, limitj] indicates that positionj is the position of the jth factory and that the jth factory can repair at most limitj robots.\n The positions of each robot are unique. The positions of each factory are also unique. Note that a robot can be in the same position as a factory initially.\n All the robots are initially broken; they keep moving in one direction. The direction could be the negative or the positive direction of the X-axis. When a robot reaches a factory that did not reach its limit, the factory repairs the robot, and it stops moving.\n At any moment, you can set the initial direction of moving for some robot. Your target is to minimize the total distance traveled by all the robots.\n Return the minimum total distance traveled by all the robots. The test cases are generated such that all the robots can be repaired.\n Note that\n All robots move at the same speed.\n If two robots move in the same direction, they will never collide.\n If two robots move in opposite directions and they meet at some point, they do not collide. They cross each other.\n If a robot passes by a factory that reached its limits, it crosses it as if it does not exist.\n If the robot moved from a position x to a position y, the distance it moved is |y - x|.\n Constraints:\n \n 1 <= robot.length, factory.length <= 100\n factory[j].length == 2\n -109 <= robot[i], positionj <= 109\n 0 <= limitj <= robot.length\n The input will be generated such that it is always possible to repair every robot.\n \"\"\"\n", "entry_point": "minimum-total-distance-traveled", "cannonical_solution": "", "test": "", "solution": "def minimumTotalDistance(robot: List[int], factory: List[List[int]]) -> int:\n \"\"\"\n There are some robots and factories on the X-axis. You are given an integer array robot where robot[i] is the position of the ith robot. You are also given a 2D integer array factory where factory[j] = [positionj, limitj] indicates that positionj is the position of the jth factory and that the jth factory can repair at most limitj robots.\n The positions of each robot are unique. The positions of each factory are also unique. Note that a robot can be in the same position as a factory initially.\n All the robots are initially broken; they keep moving in one direction. The direction could be the negative or the positive direction of the X-axis. When a robot reaches a factory that did not reach its limit, the factory repairs the robot, and it stops moving.\n At any moment, you can set the initial direction of moving for some robot. Your target is to minimize the total distance traveled by all the robots.\n Return the minimum total distance traveled by all the robots. The test cases are generated such that all the robots can be repaired.\n Note that\n All robots move at the same speed.\n If two robots move in the same direction, they will never collide.\n If two robots move in opposite directions and they meet at some point, they do not collide. They cross each other.\n If a robot passes by a factory that reached its limits, it crosses it as if it does not exist.\n If the robot moved from a position x to a position y, the distance it moved is |y - x|.\n Constraints:\n \n 1 <= robot.length, factory.length <= 100\n factory[j].length == 2\n -109 <= robot[i], positionj <= 109\n 0 <= limitj <= robot.length\n The input will be generated such that it is always possible to repair every robot.\n \"\"\"\n from typing import List\n\n def dp(i: int, j: int, k: int, memo: dict) -> int:\n if i == len(robot):\n return 0\n if (i, j, k) in memo:\n return memo[(i, j, k)]\n\n res = float('inf')\n if k < factory[j][1]:\n res = min(res, abs(robot[i] - factory[j][0]) + dp(i + 1, j, k + 1, memo))\n if j + 1 < len(factory):\n res = min(res, dp(i, j + 1, 0, memo))\n\n memo[(i, j, k)] = res\n return res\n\n robot.sort()\n factory.sort(key=lambda x: x[0])\n\n return dp(0, 0, 0, {})", "is_solved": true, "reflections": ["The implementation failed all the test cases, returning 0 instead of the expected minimum total distance. The issue lies in the way the robots are assigned to the factories and how the total distance is calculated. The current implementation assigns robots to factories in a greedy manner, which may not result in the optimal solution. Additionally, the calculation of total_distance is incorrect, as it does not account for the actual distance traveled by the robots. To fix this issue, we need to come up with a new approach that considers all possible assignments of robots to factories and calculates the total distance correctly, ensuring that the minimum total distance is returned.", "The implementation failed 5 out of the test cases provided. The issue lies in the way robots are assigned to factories and how the total distance is calculated. The current implementation divides the robots into left, right, and middle groups and assigns them to factories separately. This approach does not guarantee the minimum total distance traveled by all robots. To overcome this error, we need to change the way robots are assigned to factories and calculate the total distance. Instead of dividing the robots into groups, we should consider all possible directions for each robot and choose the one that minimizes the total distance. This can be achieved by using a more efficient algorithm, such as dynamic programming or greedy approach, to find the optimal assignment of robots to factories.", "The implementation failed 5 out of the test cases, producing incorrect minimum total distances. The issue lies in the fact that the function does not consider the optimal direction for each robot to move in order to minimize the total distance. Instead, it assigns robots to the closest factory without considering the direction. To overcome this error, we need to modify the implementation to account for the optimal direction for each robot to move in, ensuring that the total distance is minimized. This can be done by considering both left and right factories for each robot and choosing the one that results in the minimum distance traveled.", "The implementation failed 5 out of the test cases, producing incorrect results for the minimum total distance traveled by all the robots. The issue lies in the greedy approach used in the assign_robots function, which assigns robots to the closest factory with available limit. This approach does not guarantee the optimal solution, as it does not consider the overall distribution of robots and factories. To overcome this error, we need to change the algorithm to consider all possible assignments of robots to factories, taking into account the limits of each factory and minimizing the total distance traveled. This can be achieved by using dynamic programming or other optimization techniques to find the optimal assignment of robots to factories."]} +{"task_id": "next-greater-element-iv", "prompt": "def secondGreaterElement(nums: List[int]) -> List[int]:\n \"\"\"\n You are given a 0-indexed array of non-negative integers nums. For each integer in nums, you must find its respective second greater integer.\n The second greater integer of nums[i] is nums[j] such that:\n j > i\n nums[j] > nums[i]\n There exists exactly one index k such that nums[k] > nums[i] and i < k < j.\n If there is no such nums[j], the second greater integer is considered to be -1.\n For example, in the array [1, 2, 4, 3], the second greater integer of 1 is 4, 2 is 3, and that of 3 and 4 is -1.\n Return an integer array answer, where answer[i] is the second greater integer of nums[i].\n Constraints:\n \n 1 <= nums.length <= 105\n 0 <= nums[i] <= 109\n \"\"\"\n", "entry_point": "next-greater-element-iv", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 5 test cases. The issue lies in the logic of the while loop inside the for loop. When the current element is greater than the element at the top of the stack, the implementation updates the result for that index but does not continue to search for the second greater element. Instead, it breaks out of the while loop and moves on to the next element. To fix this issue, we need to remove the else block with the break statement, allowing the while loop to continue searching for the second greater element until the stack is empty or a suitable second greater element is found.", "The implementation failed 2 out of the 5 test cases, producing incorrect results for the second greater element. The issue lies in the while loop where we update the result array. When we find a greater element, we should not append the index back to the stack if the result[index] is already updated. Instead, we should only append the index back to the stack if there is no greater element found yet. To fix this error, we need to remove the else block that appends the index back to the stack. This change will ensure that we only update the result array with the correct second greater element and not overwrite it with an incorrect value.", "The implementation failed 2 out of the 5 test cases. The issue lies in the logic of finding the second greater element. The current implementation only finds the first greater element for each number in the list. The problem occurs because the code does not account for the existence of exactly one index k such that nums[k] > nums[i] and i < k < j. To fix this issue, we need to modify the logic to keep track of the first greater element and then find the second greater element accordingly. This can be done by maintaining a separate list to store the first greater elements and updating the result list only when we find the second greater element.", "The implementation failed 4 out of the 5 test cases, as it only returned the correct result for the case where all elements are equal. The issue lies in the logic of the while loop inside the for loop. The current implementation only updates the first_greater array and result array when an element is popped from the stack. However, this approach does not guarantee that the second greater element will be found for each index. To overcome this error, we need to modify the logic to ensure that we correctly find and store the second greater element for each index. One possible solution is to maintain two separate stacks for the first and second greater elements, and update the result array accordingly as we iterate through the input list."], "solution": "def secondGreaterElement(nums: List[int]) -> List[int]:\n \"\"\"\n You are given a 0-indexed array of non-negative integers nums. For each integer in nums, you must find its respective second greater integer.\n The second greater integer of nums[i] is nums[j] such that:\n j > i\n nums[j] > nums[i]\n There exists exactly one index k such that nums[k] > nums[i] and i < k < j.\n If there is no such nums[j], the second greater integer is considered to be -1.\n For example, in the array [1, 2, 4, 3], the second greater integer of 1 is 4, 2 is 3, and that of 3 and 4 is -1.\n Return an integer array answer, where answer[i] is the second greater integer of nums[i].\n Constraints:\n \n 1 <= nums.length <= 105\n 0 <= nums[i] <= 109\n \"\"\"\n first_greater_stack = []\n second_greater_stack = []\n result = [-1] * len(nums)\n\n for i in range(len(nums)):\n while first_greater_stack and nums[first_greater_stack[-1]] < nums[i]:\n index = first_greater_stack.pop()\n second_greater_stack.append(index)\n first_greater_stack.append(i)\n\n while second_greater_stack and nums[second_greater_stack[-1]] < nums[i]:\n index = second_greater_stack.pop()\n result[index] = nums[i]\n\n return result"} diff --git a/root/reflexion_leetcode_python3_gpt4_react_constraints_3_tests/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl b/root/reflexion_leetcode_python3_gpt4_react_constraints_3_tests/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl new file mode 100644 index 0000000..b329690 --- /dev/null +++ b/root/reflexion_leetcode_python3_gpt4_react_constraints_3_tests/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl @@ -0,0 +1,8 @@ +{"task_id": "minimum-reverse-operations", "prompt": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n", "entry_point": "minimum-reverse-operations", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the test cases provided. The issue lies in the calculation of the minimum number of reverse operations needed to bring the 1 to position i in the array. The current calculation does not account for the constraints imposed by the banned positions and the subarray size k. As a result, the output is incorrect for cases where the banned positions and subarray size k affect the number of reverse operations. To overcome this error, we need to modify the calculation logic to consider the constraints imposed by the banned positions and subarray size k, ensuring that the 1 in the array never goes to any of the positions in banned after each operation.", "The implementation failed 3 out of the test cases provided. The issue lies in the calculation of the minimum number of reverse operations needed to bring the 1 to position i in the array. The current approach calculates the steps based on the distance between the target position and the initial position, but it does not account for the constraints imposed by the banned positions. As a result, the calculated steps might not be the minimum number of operations required, or it might be impossible to reach the target position due to the banned positions. To overcome this error, we need to modify the algorithm to consider the banned positions while calculating the minimum number of reverse operations. This can be done by using a breadth-first search or dynamic programming approach to find the shortest path to the target position while avoiding the banned positions.", "The implementation failed 3 out of the 3 test cases, producing incorrect results. The issue lies in the logic of the BFS traversal, which does not account for the constraints of the problem, specifically the requirement that the 1 in the array should never go to any of the positions in the banned list. The current implementation only checks if the next position is not in the banned set, but it does not ensure that the 1 does not pass through a banned position during the reverse operation. To overcome this error, we need to modify the BFS traversal logic to ensure that the 1 never goes to a banned position during the reverse operation. This can be done by checking if the path between the current position and the next position contains any banned positions before adding the next position to the queue.", "The implementation failed 3 out of the test cases provided. The issue lies in the is_valid_path function, which checks if there are any banned positions between the current position and the next position. However, this approach does not account for the fact that the 1 can be moved to a non-banned position within the subarray of size k, even if there are banned positions in between. The current implementation incorrectly marks such cases as invalid, leading to incorrect results. To fix this issue, we need to modify the is_valid_path function to check if the next position is not in the banned_set, instead of checking all positions between the current and next positions. This change will ensure that we correctly identify valid paths and calculate the minimum number of reverse operations needed."], "solution": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n from collections import deque\n from typing import List\n\n banned_set = set(banned)\n ans = [-1] * n\n ans[p] = 0\n\n def is_valid_path(cur_pos, next_pos, banned_set):\n return next_pos not in banned_set\n\n for i in range(n):\n if i in banned_set:\n continue\n\n queue = deque([(p, 0)])\n visited = set([p])\n\n while queue:\n cur_pos, steps = queue.popleft()\n\n if cur_pos == i:\n ans[i] = steps\n break\n\n for direction in [-1, 1]:\n next_pos = cur_pos + direction * k\n\n if 0 <= next_pos < n and next_pos not in visited and is_valid_path(cur_pos, next_pos, banned_set):\n visited.add(next_pos)\n queue.append((next_pos, steps + 1))\n\n return ans"} +{"task_id": "collect-coins-in-a-tree", "prompt": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n", "entry_point": "collect-coins-in-a-tree", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed the test case where all vertices have coins. The issue in the implementation is that it always starts from vertex 0 and adds 2 to the total_edges for each coin found. However, this approach does not account for the optimal starting vertex and the possibility of collecting coins from the initial vertex without moving. To overcome this error, we need to modify the algorithm to find the optimal starting vertex and consider the coins that can be collected from the initial vertex without moving. This can be done by implementing a more sophisticated DFS algorithm that takes into account the distances between coins and the optimal starting vertex.", "The implementation failed 3 out of the test cases provided. The issue lies in the calculation of the total_edges and the starting_vertex. The starting_vertex is chosen as the first vertex with a coin, but this may not always result in the minimum number of edges to collect all coins. Additionally, the final return statement attempts to subtract 2 from the total_edges multiplied by 2, which may not always give the correct result. To overcome these errors, we need to reconsider the approach for selecting the starting_vertex and calculating the total_edges. We should explore alternative methods to find the optimal starting vertex and the minimum number of edges required to collect all coins and return to the initial vertex.", "The implementation failed 2 out of the 3 test cases, producing incorrect results for cases where coins are present in the tree. The issue lies in the dfs_count_coins function, which calculates the total number of edges needed to collect all coins. The current implementation adds the depth of the node to the total_edges for each coin, which leads to an overestimation of the required edges. To fix this issue, we need to modify the dfs_count_coins function to correctly calculate the minimum number of edges needed to collect all coins and return to the initial vertex. This can be done by considering the distance between the coins and the initial vertex, as well as the distance between the coins themselves.", "The implementation failed 2 out of the 3 test cases, producing incorrect results for cases where coins are present in the tree. The issue lies in the dfs_count_coins function, which calculates the total_edges based on the depth of the node. This approach does not account for the fact that we need to return to the initial vertex after collecting all the coins. To overcome this error, we need to modify the dfs_count_coins function to correctly calculate the total_edges, taking into account the requirement to return to the initial vertex. Additionally, we should also consider the optimal starting vertex to minimize the number of edges traversed."], "solution": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n from collections import defaultdict\n from typing import List\n\n def dfs(node: int, parent: int) -> int:\n nonlocal max_depth, max_depth_node\n depth = 0\n for neighbor in graph[node]:\n if neighbor != parent:\n child_depth = dfs(neighbor, node)\n if child_depth + 1 > depth:\n depth = child_depth + 1\n if depth > max_depth:\n max_depth = depth\n max_depth_node = neighbor\n return depth\n\n graph = defaultdict(list)\n for edge in edges:\n graph[edge[0]].append(edge[1])\n graph[edge[1]].append(edge[0])\n\n max_depth = -1\n max_depth_node = -1\n dfs(0, -1)\n starting_vertex = max_depth_node\n\n max_depth = -1\n max_depth_node = -1\n dfs(starting_vertex, -1)\n farthest_vertex = max_depth_node\n\n def dfs_count_coins(node: int, parent: int, depth: int) -> int:\n nonlocal total_edges\n if coins[node]:\n total_edges += depth // 2\n\n for neighbor in graph[node]:\n if neighbor != parent:\n dfs_count_coins(neighbor, node, depth + 1)\n\n total_edges = 0\n dfs_count_coins(starting_vertex, -1, 0)\n dfs_count_coins(farthest_vertex, -1, 0)\n\n # Find the optimal starting vertex\n optimal_starting_vertex = -1\n min_edges = float('inf')\n for i in range(len(coins)):\n if coins[i]:\n temp_edges = total_edges - (max_depth // 2) + dfs(i, -1) // 2\n if temp_edges < min_edges:\n min_edges = temp_edges\n optimal_starting_vertex = i\n\n # Recalculate total_edges with the optimal starting vertex\n total_edges = 0\n dfs_count_coins(optimal_starting_vertex, -1, 0)\n dfs_count_coins(farthest_vertex, -1, 0)\n\n return total_edges"} +{"task_id": "minimum-time-to-visit-a-cell-in-a-grid", "prompt": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n", "entry_point": "minimum-time-to-visit-a-cell-in-a-grid", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed one of the test cases where the bottom-right cell has a value of 0. The issue in the implementation is due to the condition next_time < grid[new_row][new_col] which skips the current iteration if the next_time is less than the value in the grid cell. However, this condition is incorrect when the grid cell value is 0, as we can visit the cell at any time. To overcome this error, we should change the condition to only skip the iteration if the next_time is less than the grid cell value and the grid cell value is not 0. This can be done by modifying the condition to if next_time < grid[new_row][new_col] and grid[new_row][new_col] != 0:. This change will ensure that we can visit cells with a value of 0 at any time, thus providing the correct minimum time required to visit the bottom-right cell.", "The implementation failed one of the test cases where the bottom-right cell has a value of 0. The issue in the implementation is due to the condition if next_time < grid[new_row][new_col] and grid[new_row][new_col] != 0:, which prevents visiting a cell with a value of 0 if the next_time is less than the cell value. However, since the cell value is 0, this condition should not be applied. To overcome this error, we should modify the condition to only check if next_time is less than the cell value when the cell value is greater than 0. This can be done by changing the condition to if grid[new_row][new_col] > 0 and next_time < grid[new_row][new_col]:. This change will ensure that we can visit cells with a value of 0 when the next_time is less than the cell value, thus providing the correct output.", "The implementation failed one of the test cases where the bottom-right cell has a value of 0. The issue in the implementation is due to the condition if grid[new_row][new_col] > 0 and next_time < grid[new_row][new_col]:, which prevents visiting a cell with a value of 0 if the next_time is less than the cell value. However, since the cell value is 0, this condition should not apply. To overcome this error, we should modify the condition to only apply when the cell value is greater than 0. We can change the condition to if grid[new_row][new_col] > 0 and next_time < grid[new_row][new_col]: to if grid[new_row][new_col] > 0 and next_time <= grid[new_row][new_col]:. This change will ensure that we can visit cells with a value of 0 when the next_time is less than or equal to the cell value, thus providing the correct output.", "The implementation failed 3 out of the test cases, returning -1 instead of the expected minimum time. The issue lies in the condition that checks if the next_time is less than or equal to grid[new_row][new_col] and then continues the loop. This condition prevents the algorithm from exploring valid paths, causing it to return -1 even when there is a valid path to the bottom-right cell. To fix this issue, we should remove the condition and allow the algorithm to explore all possible paths. By doing so, the algorithm will be able to find the correct minimum time required to visit the bottom-right cell of the matrix."], "solution": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n from heapq import heappush, heappop\n\n m, n = len(grid), len(grid[0])\n directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n visited = [[False] * n for _ in range(m)]\n visited[0][0] = True\n queue = [(0, 0, 0)]\n\n while queue:\n time, row, col = heappop(queue)\n\n if row == m - 1 and col == n - 1:\n return time\n\n for dr, dc in directions:\n new_row, new_col = row + dr, col + dc\n\n if 0 <= new_row < m and 0 <= new_col < n and not visited[new_row][new_col]:\n next_time = max(time + 1, grid[new_row][new_col])\n\n visited[new_row][new_col] = True\n heappush(queue, (next_time, new_row, new_col))\n\n return -1"} +{"task_id": "find-the-string-with-lcp", "prompt": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n", "entry_point": "find-the-string-with-lcp", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 3 test cases, producing incorrect results for the given lcp matrices. The issue lies in the nested loop where the code updates the character at the position lcp[i][j] in the word list. The current approach only considers the value of lcp[i][j] and increments the character at that position, which does not guarantee the alphabetically smallest string. To fix this issue, we need to consider the characters in the word list from the starting position to the position lcp[i][j] and update them accordingly to ensure the lexicographically smallest string is generated. This may involve updating the logic for character incrementation and considering the previous characters in the word list.", "The implementation failed 2 out of the 3 test cases, producing incorrect results for the given lcp matrices. The issue lies in the nested loop structure, which attempts to update the characters in the word based on the lcp values. However, the current approach does not properly account for the constraints of the lcp matrix and the lexicographically smallest string requirement. The implementation needs to be revised to correctly build the word based on the lcp matrix while ensuring that the resulting string is the alphabetically smallest possible. The current approach of incrementing characters in the word does not guarantee the lexicographically smallest string, and the loop structure does not properly handle the lcp constraints.", "The implementation failed 2 out of the 3 test cases, producing incorrect results. The issue lies in the nested loop where the function updates the characters in the word list based on the lcp values. The current approach does not guarantee the alphabetically smallest string, as it only increments the character at the lcp[i][j] index without considering the characters that come after it. To overcome this error, we need to revise the logic for updating the characters in the word list, ensuring that we construct the alphabetically smallest string that corresponds to the given lcp matrix.", "The implementation failed 2 out of the 3 test cases, producing incorrect results. The issue lies in the nested loop where the function tries to update the characters in the word based on the lcp values. The current approach of updating the character at lcp[i][j] using the maximum of word[lcp[i][j]] and word[lcp[j][i]] is incorrect, as it does not guarantee the alphabetically smallest string. Instead, we should update the character at lcp[i][j] by incrementing the character at the previous index (lcp[i][j] - 1) to ensure the smallest lexicographical order. To fix this issue, we need to change the logic inside the nested loop to correctly update the characters in the word based on the lcp values while maintaining the smallest lexicographical order."], "solution": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n from typing import List\n\n n = len(lcp)\n word = ['a'] * n\n\n for i in range(n):\n for j in range(i + 1, n):\n if lcp[i][j] < n:\n prev_char = word[lcp[i][j] - 1]\n if prev_char == 'z':\n word[lcp[i][j]] = 'a'\n else:\n word[lcp[i][j]] = chr(ord(prev_char) + 1)\n\n return ''.join(word)"} +{"task_id": "handling-sum-queries-after-update", "prompt": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n", "entry_point": "handling-sum-queries-after-update", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all 3 test cases, producing incorrect results for the type 3 queries. The issue lies in the type 1 query handling, where the loop iterates from l to r + 1. Since the problem statement specifies that both l and r are 0-indexed, the loop should iterate from l to r (inclusive). The current implementation flips one extra element, causing the subsequent type 2 and type 3 queries to produce incorrect results. To fix this error, we need to change the loop range in the type 1 query handling to range(l, r) instead of range(l, r + 1). This will ensure that only the specified elements are flipped, and the subsequent queries will produce the correct results.", "The implementation failed 3 out of the 3 test cases due to incorrect output values. The issue lies in the handling of type 1 queries, where the loop iterates from index l to index r, but it should iterate from index l to index r inclusive. This is because the problem statement specifies that both l and r are 0-indexed and the flip operation should be applied to the elements at indices l and r as well. To fix this error, we need to change the loop range in the type 1 query handling to range(l, r+1) instead of range(l, r). This change will ensure that the flip operation is applied correctly to the specified range, and the output values for the type 3 queries will be accurate.", "The implementation failed all 3 test cases, producing incorrect results for type 3 queries. The issue lies in the handling of type 1 queries, where the range of indices to flip is specified as (l, r+1). However, the problem statement specifies that both l and r are 0-indexed, meaning the range should be (l, r) instead. This causes the implementation to flip an extra element in nums1, leading to incorrect results for subsequent type 2 and type 3 queries. To fix this error, we need to change the range in the type 1 query handling to (l, r) by updating the for loop to for i in range(l, r). This change will ensure that we only flip the correct elements in nums1, resulting in accurate results for type 3 queries.", "The implementation failed 3 out of the 3 test cases, producing incorrect results for the type 3 queries. The issue lies in the type 1 query handling, where the loop iterates from l to r, but it should iterate from l to r+1 to include the r-th index as well. Due to this, the nums1 array is not updated correctly, leading to incorrect results for the type 3 queries. To overcome this error, we need to change the loop range in the type 1 query handling to range(l, r+1) to ensure that the r-th index is included in the update."], "solution": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n result = []\n for query in queries:\n if query[0] == 1:\n l, r = query[1], query[2]\n for i in range(l, r+1):\n nums1[i] = 1 - nums1[i]\n elif query[0] == 2:\n p = query[1]\n for i in range(len(nums1)):\n nums2[i] += nums1[i] * p\n elif query[0] == 3:\n result.append(sum(nums2))\n return result"} +{"task_id": "subsequence-with-the-minimum-score", "prompt": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n", "entry_point": "subsequence-with-the-minimum-score", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed the test case where the minimum score should be 1. The issue in the implementation is that it calculates the minimum number of characters to remove from t to make it a subsequence of s, but it does not correctly calculate the score based on the left and right indices of the removed characters. The current implementation only returns the minimum number of characters removed minus 1, which is incorrect for cases like \"abcde\" and \"aec\". To overcome this error, we need to modify the implementation to track the left and right indices of the removed characters and calculate the score accordingly. This can be done by updating the dynamic programming approach to store the left and right indices and using them to calculate the minimum score.", "The implementation failed 2 out of the 3 test cases, returning a score of 1 instead of the expected 0. The issue lies in the handling of cases where no characters need to be removed from the string t to make it a subsequence of s. The current implementation calculates the score even when no characters are removed, resulting in an incorrect minimum score. To overcome this error, we need to add a condition to check if the entire string t is a subsequence of s without any removals. If it is, we should return 0 as the minimum score. Otherwise, we can proceed with the current logic to calculate the minimum score. This change will ensure that the function returns the correct minimum score for cases where no characters need to be removed from t.", "The implementation failed 2 out of the 3 test cases, returning incorrect scores for the given strings. The issue lies in the dynamic programming approach used to calculate the minimum score. The current implementation does not correctly handle the case when no characters need to be removed from the string t to make it a subsequence of s. Instead, it calculates the score as if at least one character has been removed. To fix this issue, we need to modify the implementation to correctly handle the case when no characters need to be removed, ensuring that the minimum score is calculated accurately.", "The implementation failed the test case where the minimum score should be greater than 0. The issue lies in the calculation of the minimum score. The current implementation calculates the score as i - dp[i][m], which does not correctly represent the score as defined in the problem statement. Instead, we should calculate the score based on the indices of the removed characters in the string t. To overcome this error, we need to modify the logic for calculating the minimum score to correctly account for the indices of the removed characters in t."], "solution": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n n, m = len(s), len(t)\n dp = [[0] * (m + 1) for _ in range(n + 1)]\n\n for i in range(n + 1):\n dp[i][0] = i\n\n for i in range(1, n + 1):\n for j in range(1, m + 1):\n if s[i - 1] == t[j - 1]:\n dp[i][j] = dp[i - 1][j - 1]\n else:\n dp[i][j] = dp[i - 1][j]\n\n min_score = float('inf')\n for i in range(n + 1):\n if dp[i][m] != 0:\n left = dp[i][m] - 1\n right = i - 1\n score = right - left + 1\n min_score = min(min_score, score)\n\n if min_score == float('inf'):\n return 0\n else:\n return min_score"} +{"task_id": "minimum-number-of-visited-cells-in-a-grid", "prompt": "def minimumVisitedCells(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a 0-indexed m x n integer matrix grid. Your initial position is at the top-left cell (0, 0).\n Starting from the cell (i, j), you can move to one of the following cells:\n Cells (i, k) with j < k <= grid[i][j] + j (rightward movement), or\n Cells (k, j) with i < k <= grid[i][j] + i (downward movement).\n Return the minimum number of cells you need to visit to reach the bottom-right cell (m - 1, n - 1). If there is no valid path, return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 1 <= m, n <= 105\n 1 <= m * n <= 105\n 0 <= grid[i][j] < m * n\n grid[m - 1][n - 1] == 0\n \"\"\"\n", "entry_point": "minimum-number-of-visited-cells-in-a-grid", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 3 test cases. The issue lies in the fact that the algorithm does not account for the possibility of reaching the bottom-right cell (m - 1, n - 1) through multiple paths with different step counts. The algorithm marks a cell as visited once it is added to the queue, which prevents revisiting the cell with a potentially shorter path. To overcome this error, we should only mark a cell as visited when it is popped from the queue and update the step count accordingly. This change will ensure that we explore all possible paths and find the minimum number of cells needed to reach the bottom-right cell.", "The implementation failed 2 out of the 3 test cases. The issue lies in the fact that the algorithm does not correctly handle cases where there is no valid path to the bottom-right cell. The algorithm returns the number of steps taken to reach the bottom-right cell even when there is no valid path, as it does not differentiate between valid and invalid paths. To overcome this error, we need to modify the algorithm to track whether a valid path has been found and return -1 when no valid path exists. This can be done by adding a flag to indicate if a valid path has been found and checking this flag before returning the number of steps taken. If the flag is not set, the function should return -1, indicating that no valid path exists.", "The implementation failed 2 out of the 3 test cases. The issue lies in the fact that the algorithm does not correctly handle cases where there is no valid path to the bottom-right cell or when the optimal path requires backtracking. The current implementation uses a breadth-first search approach, which does not account for the possibility of needing to visit a cell multiple times to find the optimal path. To overcome this error, we should consider using a dynamic programming approach or a depth-first search with memoization to explore all possible paths and find the minimum number of cells needed to visit the bottom-right cell. This will ensure that the algorithm can handle cases where backtracking is required and correctly identify when there is no valid path.", "The implementation failed 3 out of the test cases, producing incorrect results. The issue lies in the dfs function, where it calculates the minimum number of cells to visit. The current implementation calculates the minimum cells by considering both rightward and downward movements separately, which may lead to double-counting some cells. To fix this issue, we need to modify the dfs function to consider both rightward and downward movements together and avoid double-counting cells. This can be done by iterating through all possible moves in a single loop and updating the minimum cells accordingly."], "solution": "def minimumVisitedCells(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a 0-indexed m x n integer matrix grid. Your initial position is at the top-left cell (0, 0).\n Starting from the cell (i, j), you can move to one of the following cells:\n Cells (i, k) with j < k <= grid[i][j] + j (rightward movement), or\n Cells (k, j) with i < k <= grid[i][j] + i (downward movement).\n Return the minimum number of cells you need to visit to reach the bottom-right cell (m - 1, n - 1). If there is no valid path, return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 1 <= m, n <= 105\n 1 <= m * n <= 105\n 0 <= grid[i][j] < m * n\n grid[m - 1][n - 1] == 0\n \"\"\"\n from functools import lru_cache\n\n m, n = len(grid), len(grid[0])\n\n @lru_cache(None)\n def dfs(i: int, j: int) -> int:\n if i == m - 1 and j == n - 1:\n return 1\n\n min_cells = float(\"inf\")\n\n for k in range(1, grid[i][j] + 1):\n if j + k < n:\n min_cells = min(min_cells, dfs(i, j + k) + 1)\n if i + k < m:\n min_cells = min(min_cells, dfs(i + k, j) + 1)\n\n return min_cells\n\n result = dfs(0, 0)\n return result if result != float(\"inf\") else -1"} +{"task_id": "rearranging-fruits", "prompt": "def minCost(basket1: List[int], basket2: List[int]) -> int:\n \"\"\"\n You have two fruit baskets containing n fruits each. You are given two 0-indexed integer arrays basket1 and basket2 representing the cost of fruit in each basket. You want to make both baskets equal. To do so, you can use the following operation as many times as you want:\n Chose two indices i and j, and swap the ith fruit of basket1 with the jth fruit of basket2.\n The cost of the swap is min(basket1[i],basket2[j]).\n Two baskets are considered equal if sorting them according to the fruit cost makes them exactly the same baskets.\n Return the minimum cost to make both the baskets equal or -1 if impossible.\n Constraints:\n \n basket1.length == bakste2.length\n 1 <= basket1.length <= 105\n 1 <= basket1[i],basket2[i] <= 109\n \"\"\"\n", "entry_point": "rearranging-fruits", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 2 out of the 3 test cases. The issue lies in the calculation of the minimum cost to make both baskets equal. The current implementation calculates the difference between the two Counters and sorts the resulting list, but it does not account for the actual cost of swapping the fruits. The current approach only considers the difference in counts and not the actual values that need to be swapped. To overcome this error, we need to modify the algorithm to correctly calculate the minimum cost of swaps by considering the actual values that need to be swapped and their corresponding costs. This can be done by finding the minimum cost pairs of fruits that need to be swapped between the two baskets.", "The implementation failed 2 out of the 3 test cases. The issue lies in the calculation of the minimum cost to make both baskets equal. The current approach calculates the difference between the two Counters and sorts the resulting list, but this does not guarantee that the correct pairs of elements are chosen for the swaps. As a result, the minimum cost is not calculated correctly, leading to incorrect outputs. To overcome this error, we need to change the approach to find the correct pairs of elements to swap and calculate the minimum cost accordingly. One possible solution is to iterate through the unique elements in the baskets and find the minimum cost for each pair of elements that need to be swapped.", "The implementation failed 2 out of the 3 test cases. The issue lies in the calculation of the minimum cost. The current implementation calculates the cost by adding the product of the minimum count of each unique element and its value. However, this approach does not account for the fact that we need to swap elements to make the baskets equal. Instead, we should calculate the cost based on the difference in counts of each unique element between the two baskets and choose the minimum cost for each swap. To overcome this error, we need to modify the logic for calculating the minimum cost by considering the difference in counts and choosing the minimum cost for each swap.", "The implementation failed all test cases due to a syntax error caused by an unexpected indent at line 15. The issue is that the import statements are indented, making them part of the function body, which is incorrect. To fix this error, we need to move the import statements outside the function definition and remove the unnecessary indentation. This change will resolve the syntax error and allow the function to execute properly."], "solution": "def minCost(basket1: List[int], basket2: List[int]) -> int:\n \"\"\"\n You have two fruit baskets containing n fruits each. You are given two 0-indexed integer arrays basket1 and basket2 representing the cost of fruit in each basket. You want to make both baskets equal. To do so, you can use the following operation as many times as you want:\n Chose two indices i and j, and swap the ith fruit of basket1 with the jth fruit of basket2.\n The cost of the swap is min(basket1[i],basket2[j]).\n Two baskets are considered equal if sorting them according to the fruit cost makes them exactly the same baskets.\n Return the minimum cost to make both the baskets equal or -1 if impossible.\n Constraints:\n \n basket1.length == bakste2.length\n 1 <= basket1.length <= 105\n 1 <= basket1[i],basket2[i] <= 109\n \"\"\"\n from collections import Counter\n from typing import List\n \n counter1 = Counter(basket1)\n counter2 = Counter(basket2)\n diff_counter = counter1 - counter2\n diff_list = list(diff_counter.elements())\n \n if len(diff_list) % 2 != 0:\n return -1\n \n diff_list.sort()\n min_cost = 0\n n = len(diff_list)\n \n for i in range(0, n, 2):\n min_cost += min(diff_list[i], diff_list[i + 1])\n \n return min_cost"} diff --git a/root/reflexion_leetcode_python3_gpt4_react_constraints_test/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl b/root/reflexion_leetcode_python3_gpt4_react_constraints_test/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl new file mode 100644 index 0000000..9de07ca --- /dev/null +++ b/root/reflexion_leetcode_python3_gpt4_react_constraints_test/leetcode-hard-py-40-uncontaminated-constraints._reflexion_5_gpt-4_pass_at_k_1_py.jsonl @@ -0,0 +1,6 @@ +{"task_id": "minimum-reverse-operations", "prompt": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n", "entry_point": "minimum-reverse-operations", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 5 out of the 5 test cases, indicating that the logic for determining the minimum number of reverse operations is incorrect. The issue lies in the is_valid_move function, which checks if there are any banned positions between the start and end positions. However, this approach does not account for the fact that the 1 can be moved to a non-banned position first and then moved to the desired position. To overcome this error, we need to modify the logic to consider all possible intermediate positions and calculate the minimum number of reverse operations required to reach the desired position without violating the banned positions constraint.", "The implementation failed 5 out of the test cases, producing incorrect results. The issue lies in the min_operations function, specifically in the logic for calculating the minimum number of reverse operations needed to bring the 1 to position i in the array. The current implementation does not account for all possible valid moves and combinations of subarray reversals, leading to incorrect results. To overcome this error, we need to revise the logic in the min_operations function to consider all possible valid moves and combinations of subarray reversals, ensuring that the minimum number of reverse operations is calculated accurately for each position i in the array.", "The implementation failed 5 out of the test cases, producing incorrect results. The issue lies in the min_operations function, specifically in the logic for calculating the minimum number of reverse operations needed to move the 1 from the source to the destination. The current implementation checks if the difference between the destination and source is divisible by k and if the number of steps is even. However, this approach does not account for all possible scenarios and may lead to incorrect results. To overcome this error, we need to revise the logic for calculating the minimum number of reverse operations, taking into account all possible subarray reversals and ensuring that the 1 does not end up in a banned position.", "The implementation failed 5 out of the 5 test cases, indicating that the logic for calculating the minimum number of reverse operations is incorrect. The issue lies in the min_operations function, which does not properly account for all possible valid moves and combinations of moves to reach the destination. The current implementation checks for a direct path from the source to the destination and then tries to find an intermediate position to reach the destination. However, this approach does not cover all possible cases and may result in incorrect or incomplete results. To overcome this error, we need to revise the logic for calculating the minimum number of reverse operations, considering all possible valid moves and combinations of moves to reach the destination. This may involve using a different algorithm or approach to find the optimal solution."], "solution": "def minReverseOperations(n: int, p: int, banned: List[int], k: int) -> List[int]:\n \"\"\"\n You are given an integer n and an integer p in the range [0, n - 1]. Representing a 0-indexed array arr of length n where all positions are set to 0's, except position p which is set to 1.\n You are also given an integer array banned containing some positions from the array. For the ith position in banned, arr[banned[i]] = 0, and banned[i] != p.\n You can perform multiple operations on arr. In an operation, you can choose a subarray with size k and reverse the subarray. However, the 1 in arr should never go to any of the positions in banned. In other words, after each operation arr[banned[i]] remains 0.\n Return an array ans where for each i from [0, n - 1], ans[i] is the minimum number of reverse operations needed to bring the 1 to position i in arr, or -1 if it is impossible.\n A subarray is a contiguous non-empty sequence of elements within an array.\n The values of ans[i] are independent for all i's.\n The reverse of an array is an array containing the values in reverse order.\n Constraints:\n \n 1 <= n <= 105\n 0 <= p <= n - 1\n 0 <= banned.length <= n - 1\n 0 <= banned[i] <= n - 1\n 1 <= k <= n\n banned[i] != p\n all values in banned are unique\n \"\"\"\n banned_set = set(banned)\n ans = [-1] * n\n\n def is_valid_move(start: int, end: int) -> bool:\n for i in range(start, end):\n if i in banned_set:\n return False\n return True\n\n def min_operations(src: int, dest: int) -> int:\n if (dest - src) % k != 0:\n return float('inf')\n steps = abs(dest - src) // k\n if steps % 2 == 0:\n return steps\n else:\n for i in range(0, n, k):\n if i not in banned_set and is_valid_move(src, i) and is_valid_move(i, dest):\n return steps + 1\n return float('inf')\n\n for i in range(n):\n if i == p:\n ans[i] = 0\n else:\n ans[i] = min_operations(p, i)\n if ans[i] == float('inf'):\n ans[i] = -1\n\n return ans"} +{"task_id": "collect-coins-in-a-tree", "prompt": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n", "entry_point": "collect-coins-in-a-tree", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases, as it does not correctly calculate the minimum number of edges needed to collect all the coins and return to the initial vertex. The issue lies in the dfs function, which only increments the total_edges by 2 for each coin found, without considering the optimal path to collect all coins. This approach does not account for the distances between the coins and the need to return to the initial vertex. To overcome this error, we need to modify the dfs function to consider the optimal path and distances between coins, as well as the requirement to return to the initial vertex. This can be done by implementing a more sophisticated traversal algorithm that takes into account the coins' positions and the tree structure.", "The implementation failed 4 out of the 5 test cases, indicating that the logic for calculating the minimum number of edges to collect all coins and return to the initial vertex is incorrect. The issue lies in the dfs function, where the depth and coins_collected values are not being updated and combined correctly. This results in an incorrect total_edges count, leading to wrong output values. To fix this issue, we need to revise the dfs function to properly account for the depth and coins_collected values, ensuring that the total_edges count accurately represents the minimum number of edges needed to collect all coins and return to the initial vertex.", "The implementation failed 3 out of the 5 test cases. The issue lies in the calculation of the total number of edges needed to collect all the coins and return to the initial vertex. The current implementation adds the two maximum depths for each node with coins, which may lead to an overestimation of the required edges. To fix this issue, we need to reevaluate the logic for calculating the total number of edges and ensure that it accurately represents the minimum number of edges needed to collect all the coins and return to the starting vertex.", "The implementation failed 4 out of the 5 test cases, producing incorrect results for cases where coins are present in multiple nodes. The issue lies in the dfs function, specifically in the calculation of total_edges. The current implementation adds max_depth1 and max_depth2 for each node with coins, which does not accurately represent the minimum number of edges needed to collect all coins and return to the initial vertex. To fix this issue, we need to reevaluate the logic for calculating total_edges, taking into account the correct traversal of the tree to minimize the number of edges traversed while collecting all coins."], "solution": "def collectTheCoins(coins: List[int], edges: List[List[int]]) -> int:\n \"\"\"\n There exists an undirected and unrooted tree with n nodes indexed from 0 to n - 1. You are given an integer n and a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given an array coins of size n where coins[i] can be either 0 or 1, where 1 indicates the presence of a coin in the vertex i.\n Initially, you choose to start at any vertex in the tree. Then, you can perform the following operations any number of times:\n Collect all the coins that are at a distance of at most 2 from the current vertex, or\n Move to any adjacent vertex in the tree.\n Find the minimum number of edges you need to go through to collect all the coins and go back to the initial vertex.\n Note that if you pass an edge several times, you need to count it into the answer several times.\n Constraints:\n \n n == coins.length\n 1 <= n <= 3 * 104\n 0 <= coins[i] <= 1\n edges.length == n - 1\n edges[i].length == 2\n 0 <= ai, bi < n\n ai != bi\n edges represents a valid tree.\n \"\"\"\n from collections import defaultdict\n from typing import List, Tuple\n\n def dfs(node: int, parent: int) -> Tuple[int, int]:\n nonlocal total_edges\n max_depth1, max_depth2 = 0, 0\n coins_collected = coins[node]\n\n for neighbor in graph[node]:\n if neighbor != parent:\n depth, neighbor_coins = dfs(neighbor, node)\n coins_collected += neighbor_coins\n\n if neighbor_coins:\n if depth + 1 > max_depth1:\n max_depth2 = max_depth1\n max_depth1 = depth + 1\n elif depth + 1 > max_depth2:\n max_depth2 = depth + 1\n\n if coins_collected:\n total_edges += max_depth1 + max_depth2\n\n return max_depth1, coins_collected\n\n graph = defaultdict(list)\n for edge in edges:\n graph[edge[0]].append(edge[1])\n graph[edge[1]].append(edge[0])\n\n total_edges = 0\n max_depth, coins_collected = dfs(0, -1)\n if coins_collected:\n total_edges -= max_depth\n\n return total_edges"} +{"task_id": "minimum-time-to-visit-a-cell-in-a-grid", "prompt": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n", "entry_point": "minimum-time-to-visit-a-cell-in-a-grid", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 3 out of the 5 test cases. The issue lies in the fact that the algorithm marks a cell as visited before checking if it can actually be visited at the current time. This causes the algorithm to not explore alternative paths that could lead to a better solution. To overcome this error, we should only mark a cell as visited if the new_time is greater than or equal to the grid value at that cell. This can be done by moving the visited[new_row][new_col] = True line inside the condition checking if new_time is greater than or equal to grid[new_row][new_col]. This change will ensure that the algorithm only marks cells as visited when they can actually be visited at the current time, thus exploring all possible paths and finding the correct solution.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the visited matrix and push the new position into the queue. The current implementation checks if new_time >= grid[new_row][new_col] before marking the cell as visited and pushing it into the queue. However, this condition is incorrect, as it allows visiting cells with a time less than the required minimum time. To fix this issue, we should change the condition to new_time == grid[new_row][new_col], ensuring that we only visit cells when the time is exactly equal to the required minimum time. This change will ensure that the algorithm correctly calculates the minimum time required to visit the bottom-right cell of the matrix.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the visited matrix and push the new cell into the queue. The current implementation only updates the visited matrix and pushes the new cell when new_time is equal to grid[new_row][new_col]. However, this condition is incorrect, as it should update the visited matrix and push the new cell when new_time is greater than or equal to grid[new_row][new_col]. To fix this error, we need to change the condition to if new_time >= grid[new_row][new_col]:. This change will ensure that the visited matrix is updated correctly and the new cells are pushed into the queue when the time constraint is satisfied, thus providing the correct output for the test cases.", "The implementation failed 3 out of the 5 test cases. The issue lies in the condition to update the visited matrix and push the new position into the queue. The current implementation checks if new_time >= grid[new_row][new_col] before marking the cell as visited and pushing it into the queue. However, this condition is not sufficient to ensure the optimal path, as it may visit cells with higher time values earlier, leading to suboptimal results. To overcome this error, we should only mark the cell as visited and push it into the queue if the new_time is less than the current minimum time required to visit that cell. We can maintain a separate matrix to store the minimum time required to visit each cell and update it accordingly. This change will ensure that we only visit cells when it leads to an optimal path, thus providing the correct minimum time required to visit the bottom-right cell."], "solution": "def minimumTime(grid: List[List[int]]) -> int:\n \"\"\"\n You are given a m x n matrix grid consisting of non-negative integers where grid[row][col] represents the minimum time required to be able to visit the cell (row, col), which means you can visit the cell (row, col) only when the time you visit it is greater than or equal to grid[row][col].\n You are standing in the top-left cell of the matrix in the 0th second, and you must move to any adjacent cell in the four directions: up, down, left, and right. Each move you make takes 1 second.\n Return the minimum time required in which you can visit the bottom-right cell of the matrix. If you cannot visit the bottom-right cell, then return -1.\n Constraints:\n \n m == grid.length\n n == grid[i].length\n 2 <= m, n <= 1000\n 4 <= m * n <= 105\n 0 <= grid[i][j] <= 105\n grid[0][0] == 0\n \"\"\"\n from heapq import heappush, heappop\n\n m, n = len(grid), len(grid[0])\n directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n visited = [[False] * n for _ in range(m)]\n min_time = [[float('inf')] * n for _ in range(m)]\n visited[0][0] = True\n min_time[0][0] = 0\n queue = [(0, 0, 0)]\n\n while queue:\n time, row, col = heappop(queue)\n if row == m - 1 and col == n - 1:\n return time\n\n for dr, dc in directions:\n new_row, new_col = row + dr, col + dc\n if 0 <= new_row < m and 0 <= new_col < n:\n new_time = max(time + 1, grid[new_row][new_col])\n if new_time < min_time[new_row][new_col]:\n visited[new_row][new_col] = True\n min_time[new_row][new_col] = new_time\n heappush(queue, (new_time, new_row, new_col))\n\n return -1"} +{"task_id": "find-the-string-with-lcp", "prompt": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n", "entry_point": "find-the-string-with-lcp", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 4 out of the 5 test cases, producing incorrect output for the given lcp matrices. The issue lies in the nested loop structure, which attempts to update the characters in the word based on the lcp values. However, the current approach does not properly account for the lexicographically smallest string requirement and updates the characters incorrectly. To overcome this error, we need to revise the algorithm to correctly build the lexicographically smallest string based on the given lcp matrix, ensuring that the characters are updated according to the constraints and the longest common prefix lengths.", "The implementation failed 4 out of the 5 test cases, producing incorrect output for cases where the lcp matrix has more complex patterns. The issue lies in the logic used to update the characters in the word based on the max_lcp value. The current implementation only updates the character at position j when lcp[i][j] is equal to max_lcp, which does not account for all possible cases. To overcome this error, we need to revise the logic to correctly update the characters in the word based on the lcp matrix values, ensuring that the resulting word corresponds to the given lcp matrix and is alphabetically smallest.", "The implementation failed 4 out of the 5 test cases, producing incorrect results. The issue lies in the nested loop where the algorithm tries to build the word based on the lcp matrix. The current approach does not properly handle the cases where the common prefix length is greater than 1, leading to incorrect character assignments in the word. To fix this issue, we need to revise the algorithm to correctly handle cases with common prefix lengths greater than 1, ensuring that the resulting word is lexicographically smallest and corresponds to the given lcp matrix.", "The implementation failed 4 out of the 5 test cases, producing incorrect output for the given lcp matrices. The issue lies in the logic for updating the characters in the word list. The current implementation updates the characters based on the difference between lcp[i][j+1] and lcp[i][j], which does not guarantee the correct character for the alphabetically smallest string. To overcome this error, we need to revise the logic for updating the characters in the word list, ensuring that we construct the alphabetically smallest string that corresponds to the given lcp matrix."], "solution": "def findTheString(lcp: List[List[int]]) -> str:\n \"\"\"\n We define the lcp matrix of any 0-indexed string word of n lowercase English letters as an n x n grid such that:\n lcp[i][j] is equal to the length of the longest common prefix between the substrings word[i,n-1] and word[j,n-1].\n Given an n x n matrix lcp, return the alphabetically smallest string word that corresponds to lcp. If there is no such string, return an empty string.\n A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, \"aabd\" is lexicographically smaller than \"aaca\" because the first position they differ is at the third letter, and 'b' comes before 'c'.\n Constraints:\n \n 1 <= n == lcp.length == lcp[i].length <= 1000\n 0 <= lcp[i][j] <= n\n \"\"\"\n from typing import List\n\n n = len(lcp)\n word = ['a'] * n\n\n for i in range(n):\n for j in range(i + 1, n):\n if lcp[i][j] == 0:\n word[j] = chr(ord(word[j - 1]) + 1)\n else:\n common_prefix_length = lcp[i][j]\n next_char_idx = ord(word[j - 1]) + common_prefix_length + 1\n if next_char_idx < ord('z'):\n word[j] = chr(next_char_idx)\n else:\n word[j] = 'z'\n\n return ''.join(word)"} +{"task_id": "handling-sum-queries-after-update", "prompt": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n", "entry_point": "handling-sum-queries-after-update", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed all test cases involving type 2 and type 3 queries. The issue lies in the handling of type 1 queries, where the range of indices to flip is not being handled correctly. The current implementation flips the values from index l to index r, inclusive. However, the problem statement specifies that the values should be flipped from index l to index r, exclusive. To fix this error, we need to change the range in the for loop for type 1 queries to exclude the r index. This can be done by modifying the loop to for i in range(l, r) instead of for i in range(l, r + 1). This change will ensure that the values are flipped according to the problem statement, and the subsequent type 2 and type 3 queries will produce the correct results.", "The implementation failed all the test cases involving type 1 and type 2 queries. The issue lies in the type 1 query handling, where the loop iterates from l to r, but it should iterate from l to r+1 to include the r-th index as well. This causes the nums1 array to not be updated correctly, leading to incorrect results for type 2 and type 3 queries. To overcome this error, we need to change the loop range in the type 1 query handling to range(l, r+1) to include the r-th index in the flipping operation. This change will ensure that the nums1 array is updated correctly, and the subsequent queries will produce the expected results.", "The implementation failed all the test cases involving type 2 and type 3 queries. The issue lies in the handling of type 1 queries, where the range of indices to flip is from l to r inclusive. However, the implementation flips the values from l to r+1, which is incorrect. To fix this error, we need to change the range in the for loop for type 1 queries to be from l to r instead of l to r+1. This can be done by modifying the for loop to for i in range(l, r). This change will ensure that the correct range of indices is flipped, and the subsequent type 2 and type 3 queries will produce the expected results.", "The implementation failed all the test cases involving type 1 and type 2 queries. The issue lies in the type 1 query handling, where the loop iterates from l to r, but it should iterate from l to r+1 to include the r-th index as well. This causes the nums1 array to not be updated correctly, leading to incorrect results for type 2 and type 3 queries. To overcome this error, we need to change the loop range in the type 1 query handling to range(l, r+1) to include the r-th index in the flipping operation. This change will ensure that the nums1 array is updated correctly, and the subsequent queries will produce the expected results."], "solution": "def handleQuery(nums1: List[int], nums2: List[int], queries: List[List[int]]) -> List[int]:\n \"\"\"\n You are given two 0-indexed arrays nums1 and nums2 and a 2D array queries of queries. There are three types of queries:\n For a query of type 1, queries[i] = [1, l, r]. Flip the values from 0 to 1 and from 1 to 0 in nums1 from index l to index r. Both l and r are 0-indexed.\n For a query of type 2, queries[i] = [2, p, 0]. For every index 0 <= i < n, set nums2[i] = nums2[i] + nums1[i] * p.\n For a query of type 3, queries[i] = [3, 0, 0]. Find the sum of the elements in nums2.\n Return an array containing all the answers to the third type queries.\n Constraints:\n \n 1 <= nums1.length,nums2.length <= 105\n nums1.length = nums2.length\n 1 <= queries.length <= 105\n queries[i].length = 3\n 0 <= l <= r <= nums1.length - 1\n 0 <= p <= 106\n 0 <= nums1[i] <= 1\n 0 <= nums2[i] <= 109\n \"\"\"\n result = []\n \n for query in queries:\n if query[0] == 1:\n l, r = query[1], query[2]\n for i in range(l, r+1):\n nums1[i] = 1 - nums1[i]\n elif query[0] == 2:\n p = query[1]\n for i in range(len(nums1)):\n nums2[i] += nums1[i] * p\n elif query[0] == 3:\n result.append(sum(nums2))\n \n return result"} +{"task_id": "subsequence-with-the-minimum-score", "prompt": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n", "entry_point": "subsequence-with-the-minimum-score", "cannonical_solution": "", "test": "", "is_solved": false, "reflections": ["The implementation failed 4 out of the 5 test cases, producing incorrect minimum scores. The issue lies in the dynamic programming approach used, which calculates the minimum number of characters to remove from 't' to make it a subsequence of 's', but does not account for the score calculation based on the left and right indices of the removed characters. As a result, the function returns 0 for cases where the score should be greater than 0. To fix this issue, we need to modify the dynamic programming approach to consider the score calculation based on the left and right indices of the removed characters, and update the minimum score accordingly.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or 'inf' when it should have returned a valid score. The issue lies in the dynamic programming logic, specifically in the else block where the score is updated. The current implementation adds 1 to the score, which is incorrect. Instead, we should be updating the score with the value from the previous row (dp[i - 1][j]) without adding 1. This will ensure that the score is calculated correctly based on the minimum index among all removed characters. To fix this error, we need to change the else block to update dp[i][j] with dp[i - 1][j] instead of adding 1.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or 'inf' when it should have returned a valid score. The issue lies in the dynamic programming approach used to calculate the minimum score. The current implementation does not properly account for the left and right indices of the removed characters, leading to incorrect score calculations. To overcome this error, we need to revise the dynamic programming approach to correctly track the left and right indices of the removed characters and calculate the score accordingly. This will ensure that the minimum score is calculated correctly for all test cases.", "The implementation failed 5 out of the 5 test cases, producing incorrect results or returning -1 when a valid solution exists. The issue lies in the dynamic programming approach used to calculate the minimum score. The current implementation calculates the minimum number of characters to remove, but it does not properly account for the score calculation, which is based on the difference between the right and left indices of the removed characters. To fix this issue, we need to modify the dynamic programming approach to track the left and right indices of the removed characters and calculate the score accordingly. This will ensure that the correct minimum score is returned for each test case."], "solution": "def minimumScore(s: str, t: str) -> int:\n \"\"\"\n You are given two strings s and t.\n You are allowed to remove any number of characters from the string t.\n The score of the string is 0 if no characters are removed from the string t, otherwise:\n Let left be the minimum index among all removed characters.\n Let right be the maximum index among all removed characters.\n Then the score of the string is right - left + 1.\n Return the minimum possible score to make t a subsequence of s.\n A subsequence of a string is a new string that is formed from the original string by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (i.e., \"ace\" is a subsequence of \"abcde\" while \"aec\" is not).\n Constraints:\n \n 1 <= s.length, t.length <= 105\n s and t consist of only lowercase English letters.\n \"\"\"\n n, m = len(s), len(t)\n dp = [[float('inf')] * (m + 1) for _ in range(n + 1)]\n\n for i in range(n + 1):\n dp[i][0] = 0\n\n for i in range(1, n + 1):\n for j in range(1, m + 1):\n if s[i - 1] == t[j - 1]:\n dp[i][j] = min(dp[i - 1][j - 1], dp[i - 1][j] + 1)\n else:\n dp[i][j] = dp[i - 1][j]\n\n min_score = float('inf')\n for i in range(n + 1):\n for j in range(m + 1):\n if dp[i][j] != float('inf'):\n left = j - dp[i][j]\n right = j - 1\n score = right - left + 1\n min_score = min(min_score, score)\n\n return min_score if min_score != float('inf') else -1"} diff --git a/run_reflexion_py_leet.sh b/run_reflexion_py_leet.sh index 7925e9f..d591365 100755 --- a/run_reflexion_py_leet.sh +++ b/run_reflexion_py_leet.sh @@ -1,7 +1,7 @@ python main.py \ - --run_name "reflexion_leetcode_python3_gpt4" \ + --run_name "reflexion_leetcode_python3_gpt4_react_constraints" \ --root_dir "root" \ - --dataset_path ./executors/leetcode_env/leetcode_dataset/data/humaneval/leetcode-hard-py-40-uncontaminated.jsonl \ + --dataset_path ./executors/leetcode_env/leetcode_dataset/data/humaneval/leetcode-hard-py-40-uncontaminated-constraints.jsonl \ --strategy "reflexion" \ --language "py" \ --model "gpt-4" \