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# https://github.com/Naman-ntc/codescratch/blob/main/evaluation/bigcode-evaluation-harness/lm_eval/tasks/custom_metrics/apps_custom_metrics/utils.py
import os
import sys
sys.set_int_max_str_digits(50000)
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import json
import multiprocessing
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
from tqdm import tqdm
from lcb_runner.evaluation.testing_util import run_test
from lcb_runner.evaluation.pass_k_utils import compute_metrics_from_results
def _temp_run(sample, generation, debug, result, metadata_list, timeout):
res, metadata = run_test(sample, test=generation, debug=debug, timeout=timeout)
result.append(res)
metadata_list.append(metadata)
def check_correctness(sample, generation, timeout, debug=True):
"""Check correctness of code generation with a global timeout.
The global timeout is to catch some extreme/rare cases not handled by the timeouts
inside `run_test`"""
manager = multiprocessing.Manager()
result = manager.list()
metadata_list = manager.list()
p = multiprocessing.Process(
target=_temp_run,
args=(sample, generation, debug, result, metadata_list, timeout),
)
p.start()
p.join(
timeout=(timeout + 1) * len(json.loads(sample["input_output"])["inputs"]) + 5
)
try:
os.kill(p.pid, 9) # Force to kill the process by PID for safety
except:
None
if p.is_alive():
p.kill()
if not result:
in_outs = json.loads(sample["input_output"])
# consider that all tests failed
result = [[-1 for i in range(len(in_outs["inputs"]))]]
if debug:
print(f"global timeout")
return result[0], metadata_list[0]
def evaluate_generations_by_problem(args):
problem_generations: list[str] = args[0]
sample = args[1]
debug: bool = args[2]
timeout: int = args[3]
res = []
metadata = []
for o_idx, o in enumerate(problem_generations):
curr_res = [-2]
try:
curr_res, curr_metadata = check_correctness(
sample, o, timeout=timeout, debug=debug
)
if debug:
print(f"\nSuccessful compilation of task {o_idx}!")
fixed = []
for e in curr_res:
if isinstance(e, np.ndarray):
e = e.item(0)
if isinstance(e, np.bool_):
e = bool(e)
fixed.append(e)
curr_res = fixed
if not np.all(curr_res):
if debug:
print(f"Results were not True for all test cases {curr_res=}\n")
except Exception as e:
if debug:
print(f"Compilation failed, test framework exception = {repr(e)}{e}\n")
# break
curr_metadata = {
"error": repr(e),
"error_code": -5,
"error_message": "TestRunnerError",
}
finally:
assert isinstance(curr_res, list), curr_res
assert isinstance(curr_metadata, dict), curr_metadata
res.append(curr_res)
metadata.append(curr_metadata)
if debug:
for i, r in enumerate(problem_generations):
print("Sample\n")
print(r)
print("\n")
print("Result\n")
print(res[i])
print("*" * 30 + "\n\n")
return res, metadata
def evaluate_generations(
samples_list: list,
generations_list: list[list[str]],
debug: bool = False,
num_process_evaluate: int = 16,
timeout=6,
):
"""We take the list of code generations and try to compile them
and the run their corresponding unit tests which are retrieved from the APPS dataset.
Args:
generations: list of code generations (same order as samples in APPS dataset)
level: difficulty level used in the generation, can be "all", "introductory", "interview" or "competition"
Returns:
results: dictionary of results, key is the problem index, value is a list of results for each generation
"""
# generations are code generations in the same order of the dataset
inputs = [
[(generations_list[index], samples_list[index], debug, timeout), index]
for index in range(len(generations_list))
]
with tqdm(total=len(inputs)) as pbar:
with ProcessPoolExecutor(
max_workers=1 if debug else num_process_evaluate
) as executor:
futures = {
executor.submit(evaluate_generations_by_problem, arg): index
for arg, index in inputs
}
results = {}
metadata = {}
for future in as_completed(futures):
index = futures[future]
results[index], metadata[index] = future.result()
pbar.update(1)
assert len(results) == len(
inputs
), f"results = {len(results)} inputs = {len(inputs)} {results=}"
# results = {i: r for r, (_, i) in zip(results, inputs)}
return results, metadata
def codegen_metrics(
samples_list,
generations_list,
k_list=[1, 5, 10, 20, 40, 50, 75, 100, 125, 150, 200, 500, 1000],
num_process_evaluate=16,
timeout=6,
debug=False,
):
samples_linear = []
generations_linear = []
remap_index = []
results = defaultdict(list)
metadatas = defaultdict(list)
for idx, (sample, generation_list) in enumerate(
zip(samples_list, generations_list)
):
assert isinstance(generation_list, list), generations_list[0]
for generation in generation_list:
assert isinstance(generation, str), generations_list[0]
samples_linear.append(sample)
generations_linear.append([generation])
remap_index.append(idx)
print(f"Evaluating {len(samples_linear)}...")
results_linear, metadatas_linear = evaluate_generations(
samples_linear,
generations_linear,
debug=debug,
num_process_evaluate=num_process_evaluate,
timeout=timeout,
)
for idx, sub_results in sorted(results_linear.items(), key=lambda x: x[0]):
results[remap_index[idx]].append(sub_results[0])
for idx, sub_metadatas in sorted(metadatas_linear.items(), key=lambda x: x[0]):
metadatas[remap_index[idx]].append(sub_metadatas[0])
metrics = compute_metrics_from_results(results, k_list=k_list)
final_metadata = []
for key in sorted(list(metadatas.keys())):
final_metadata.append(metadatas[key])
for i in range(len(final_metadata)):
if type(final_metadata[i]) is not list:
final_metadata[i] = [json.dumps(final_metadata[i])]
else:
final_metadata[i] = [json.dumps(x) for x in final_metadata[i]]
assert len(final_metadata[i]) == len(
generations_list[0]
), f"{len(final_metadata[i])=}"
return [metrics, results, final_metadata]
if __name__ == "__main__":
# print(
# check_correctness(
# {
# "input_output": json.dumps(
# {
# "inputs": [
# json.dumps([1] * 100000)
# + "\n"
# + json.dumps([100000, -100000] * (100000 // 2))
# ],
# "outputs": [json.dumps([100000, 0] * (100000 // 2))],
# "fn_name": "mostFrequentIDs",
# }
# )
# },
# "class Solution:\n def mostFrequentIDs(self, nums: List[int], freq: List[int]) -> List[int]:\n from collections import defaultdict\n \n # Count of each ID\n count = defaultdict(int)\n # How many IDs exist for a given frequency\n freq_of_count = defaultdict(int)\n \n max_freq = 0\n ans = []\n \n for i in range(len(nums)):\n x = nums[i]\n change = freq[i]\n \n old_freq = count[x]\n new_freq = old_freq + change\n \n # If there was an old frequency, decrease its usage\n if old_freq > 0:\n freq_of_count[old_freq] -= 1\n if freq_of_count[old_freq] == 0:\n del freq_of_count[old_freq]\n \n # Update with the new frequency\n count[x] = new_freq\n freq_of_count[new_freq] += 1\n \n # Update max_freq if needed\n if new_freq > max_freq:\n max_freq = new_freq\n \n # If the collection at max_freq is empty, reduce max_freq until we find a non-empty bin\n while max_freq > 0 and max_freq not in freq_of_count:\n max_freq -= 1\n \n # If the collection is empty, max_freq will be 0\n ans.append(max_freq)\n \n return ans",
# 6,
# debug=True,
# )
# )
print(
check_correctness(
{
"input_output": json.dumps(
{
"inputs": ")))))",
"outputs": "0",
},
)
},
"\nMOD = 998244353\n\nS = input().strip()\nn = len(S)\n\nif n % 2 != 0:\n print(0)\n exit()\n\n# Initialize DP table\ndp = [[0] * (n + 2) for _ in range(n + 1)]\ndp[0][0] = 1\n\nfor i in range(1, n + 1):\n c = S[i-1]\n for b in range(n + 1):\n if dp[i-1][b] == 0:\n continue\n if c == '(':\n new_b = b + 1\n if new_b <= n:\n dp[i][new_b] = (dp[i][new_b] + dp[i-1][b]) % MOD\n elif c == ')':\n if b > 0:\n new_b = b - 1\n dp[i][new_b] = (dp[i][new_b] + dp[i-1][b]) % MOD\n else: # '?'\n # Replace with '('\n new_b = b + 1\n if new_b <= n:\n dp[i][new_b] = (dp[i][new_b] + dp[i-1][b]) % MOD\n # Replace with ')'\n if b > 0:\n new_b = b - 1\n dp[i][new_b] = (dp[i][new_b] + dp[i-1][b]) % MOD\n\nprint(dp[n][0] % MOD)\n",
6,
debug=True,
)
)
|