From fc6d57ffb8d5ddb5820fcc00b5491a585c259ebc Mon Sep 17 00:00:00 2001
From: Yuren Hao <yurenh2@timan108.cs.illinois.edu>
Date: Thu, 4 Sep 2025 22:16:22 -0500
Subject: Initial commit

---
 code_eval/OpenCodeEval/benchmark/understandml.py | 152 +++++++++++++++++++++++
 1 file changed, 152 insertions(+)
 create mode 100644 code_eval/OpenCodeEval/benchmark/understandml.py

(limited to 'code_eval/OpenCodeEval/benchmark/understandml.py')

diff --git a/code_eval/OpenCodeEval/benchmark/understandml.py b/code_eval/OpenCodeEval/benchmark/understandml.py
new file mode 100644
index 0000000..590db3f
--- /dev/null
+++ b/code_eval/OpenCodeEval/benchmark/understandml.py
@@ -0,0 +1,152 @@
+import os
+
+from OpenCodeEval.benchmark.base import Benchmark, PYTHON_STOP, PYTHON_IMPORTS
+from OpenCodeEval.utils import program_extract, stream_jsonl
+from OpenCodeEval.eval.func_eval import check_correctness
+from OpenCodeEval.eval.sanitize import sanitize
+
+from typing import List, Literal
+
+from typing import *
+from functools import *
+from collections import *
+from itertools import *
+from heapq import *
+from bisect import *
+from string import *
+from operator import *
+from math import *
+
+import numpy as np
+from numpy import *
+
+import datetime
+import copy
+
+inf = float('inf')
+
+if os.environ.get("MAX_LINES"):
+    MAX_LINES = int(os.environ.get("MAX_LINES"))
+else:
+    MAX_LINES = 200
+
+def base_prompt(data):
+    prompt = 'You are an expert Python programmer, and here is your task:\n'
+    prompt = prompt + f'# Task: {data["title"]}\n'
+    prompt = prompt + f'# Description:\n{data["description"]}\n'
+    # prompt = prompt + f'# Examples:\n'
+    # for example_idx, (example, reasoning) in enumerate(zip(data["examples"], data["reasoning"])):
+    #     prompt = prompt + f'## Example {example_idx + 1}:\n'
+    #     prompt = prompt + f'### Input:\n{example["input"]}\n'
+    #     prompt = prompt + f'### Output:\n{example["output"]}\n'
+    #     prompt = prompt + f'### Reasoning:\n{reasoning}\n'
+    input_code = (data["import_code"] + "\n" + data["starter_code"]).strip()
+    prompt = prompt + f'# Your code should start with:\n```python\n{input_code}\n```\n'
+    if data['output_constrains'].strip():
+        prompt = prompt + f'# Output Constraints:\n{data["output_constrains"].strip()}\n'
+
+    return prompt
+
+
+
+class understandml(Benchmark):
+
+    name: str = "understandml"
+
+    imports_code = PYTHON_IMPORTS
+    chat_stop = PYTHON_STOP
+    base_stop = ['\n"""', "\nassert"]
+
+    def __init__(
+        self,
+        split: Literal["human", "model"] = "model",
+        time_out: float = 3.0,
+        prompt_type: str = "Instruction"
+    ):
+
+        super().__init__()
+
+        self.split = split
+        self.time_out = time_out
+        self.prompt_type = prompt_type
+
+        self.path = os.path.join(self.path, f"{self.name}/{self.split}_benchmark.jsonl")
+        self.tasks = self.get_task()
+    
+    def get_task(self):
+        """
+        Get the task data from the jsonl file into a dictionary.
+        """
+
+        tasks = {}
+        
+        for task_data in stream_jsonl(filename = self.path):
+            task_id = int(task_data["id"])
+            
+            tasks[task_id] = task_data
+        
+        return tasks
+    
+    def get_prompt(self):
+        """
+        Builds the prompt for the LM to generate from.
+        """
+
+        assert self.prompt_type == "Instruction", "Prompt type must be Instruction for mbpp"
+
+        prompts = []
+
+        for task_id, task_data in self.tasks.items():
+                
+            prompt = base_prompt(task_data)
+            prompts.append({
+                'task_id': task_id,
+                'prompt': prompt
+            })
+
+        return prompts
+
+    def postprocess_generation(self, generation):
+        """
+        Postprocess the generations.
+        """
+
+        entry_point = self.tasks[generation['task_id']]["entry_point"]
+
+        try:
+            completion = '\n'.join(generation['completion'].splitlines()[-MAX_LINES:])
+
+            if '</think>' in completion:
+                completion = completion.split('</think>')[1]
+            
+            solution = sanitize(completion, entry_point)
+        except Exception:
+            solution = program_extract(generation['completion'], program="python", mode="all")
+
+        result = dict(
+            task_id = generation['task_id'],
+            completion_id = generation['completion_id'],
+            solution = solution
+        )
+
+        return result
+    
+    def process_results(self, solution):
+        """
+        Takes the list of LM generations and evaluates them against the test cases
+        """
+
+        task_data = self.tasks[solution['task_id']]
+
+        code =  (
+                    task_data['import_code'] + "\n"
+                    + solution['solution'] + "\n"
+                    + "\n".join(task_data['test_cases'])
+                )
+        
+        result = check_correctness(solution['task_id'],
+                                   solution['completion_id'],
+                                   code,
+                                   self.time_out)
+        
+        return result
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