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01f199c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from .Dataset import Dataset
from evaluations.evalute import xcode_evaluate, contest_evaluate_public_tests
from constants.paths import *
class XCodeDataset(Dataset):
def __init__(
self,
path: str = XCODE_VALIDATION_DATA_PATH,
):
super().__init__(path)
self.id_key = "src_uid"
def evaluate_sample_io(
self,
item: dict,
cur_imp: str,
language: str,
):
sample_io = []
for input, output in zip(item["sample_inputs"], item["sample_outputs"]):
sample_io.append({
"input": input,
"output": [output]
})
return contest_evaluate_public_tests(
generated_code=cur_imp,
id=item[self.id_key],
tests=sample_io,
lang=language
)
def evaluate(
self,
item: dict,
cur_imp: str,
language: str,
):
return xcode_evaluate(
generated_code=cur_imp,
src_uid=item["src_uid"],
lang=language
)
@staticmethod
def get_prompt(item):
return f"Problem Description:\n{item['description']}\nInput Specification:\n{item['input_spec']}\nOutput Specification:\n{item['output_spec']}\nSample Inputs: {item['sample_inputs']}\nSample Outputs: {item['sample_outputs']}\nNote: {item['notes']}\nTake input from: {item['input_from']}\nGive output to: {item['output_to']}\nTime Limit: {item['time_limit']}\nMemory Limit: {item['memory_limit']}\n\nNote: If you are writing a function then after the function definition take input from using `input()` function, call the function with specified parameters and finally print the output of the function."
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