---
license: apache-2.0
dataset_info:
  features:
  - name: task_id
    dtype: string
  - name: prompt
    dtype: string
  - name: entry_point
    dtype: string
  - name: test
    dtype: string
  - name: description
    dtype: string
  - name: language
    dtype: string
  - name: canonical_solution
    sequence: string
  splits:
  - name: train
    num_bytes: 505355
    num_examples: 161
  download_size: 174830
  dataset_size: 505355
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Evaluation summary

We introduce HumanEval for Kotlin, created from scratch by human experts.
All HumanEval solutions and tests are written by an expert olympiad programmer with 6 years experience in Kotlin, and independently checked by a programmer with 4 years experience in Kotlin. 
The tests we implement are eqivalent to the original HumanEval tests for Python, and we fix the prompt signatures to address the generic variable signature we describe above.

# How to use 

The evaluation presented as dataset which is prepared in a format suitable for MXEval and can be easily integrated into the MXEval pipeline.

During the code generation step, we use early stopping on the `}\n}` sequence to expedite the process. We also perform some code post-processing before evaluation—specifically, we remove all comments and signatures.

The early stopping method, post-processing steps, and evaluation code are available in the example below.

```python
import json
import re

from datasets import load_dataset
import jsonlines
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    StoppingCriteria,
    StoppingCriteriaList,
)
from tqdm import tqdm 
from mxeval.evaluation import evaluate_functional_correctness


class StoppingCriteriaSub(StoppingCriteria):
    def __init__(self, stops, tokenizer):
        (StoppingCriteria.__init__(self),)
        self.stops = rf"{stops}"
        self.tokenizer = tokenizer

    def __call__(
        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
    ) -> bool:
        last_three_tokens = [int(x) for x in input_ids.data[0][-3:]]
        decoded_last_three_tokens = self.tokenizer.decode(last_three_tokens)

        return bool(re.search(self.stops, decoded_last_three_tokens))


def generate(problem):
    criterion = StoppingCriteriaSub(stops="\n}\n", tokenizer=tokenizer)
    stopping_criteria = StoppingCriteriaList([criterion])
    
    problem = tokenizer.encode(problem, return_tensors="pt").to('cuda')
    sample = model.generate(
        problem,
        temperature=0.1,
        max_new_tokens=256,
        min_new_tokens=128,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=False,
        num_beams=1,
        stopping_criteria=stopping_criteria,
    )
    
    answer = tokenizer.decode(sample[0], skip_special_tokens=True)
    return answer


def clean_asnwer(code):
    # Clean comments
    code_without_line_comments = re.sub(r"//.*", "", code)
    code_without_all_comments = re.sub(
        r"/\*.*?\*/", "", code_without_line_comments, flags=re.DOTALL
    )
    #Clean signatures
    lines = code.split("\n")
    for i, line in enumerate(lines):
        if line.startswith("fun "):
            return "\n".join(lines[i + 1:])
            
    return code


model_name = "JetBrains/CodeLlama-7B-Kexer"
dataset = load_dataset("jetbrains/Kotlin_HumanEval")['train']
problem_dict = {problem['task_id']: problem for problem in dataset}

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to('cuda')
tokenizer = AutoTokenizer.from_pretrained(model_name)

output = []
for key in tqdm(list(problem_dict.keys()), leave=False):
    problem = problem_dict[key]["prompt"]
    answer = generate(problem)
    answer = clean_asnwer(answer)
    output.append({"task_id": key, "completion": answer, "language": "kotlin"})

output_file = f"answers"
with jsonlines.open(output_file, mode="w") as writer:
    for line in output:
        writer.write(line)

evaluate_functional_correctness(
    sample_file=output_file,
    k=[1],
    n_workers=16,
    timeout=15,
    problem_file=problem_dict,
)

with open(output_file + '_results.jsonl') as fp:
    total = 0
    correct = 0
    for line in fp:
        sample_res = json.loads(line)
        print(sample_res)
        total += 1
        correct += sample_res['passed']

print(f'Pass rate: {correct/total}')

```


# Results:

We evaluated multiple coding models using this benchmark, and the results are presented in the table below.