src/datasets/XCodeDataset.py

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."