update code and kk eval

1 files changed, 114 insertions, 0 deletions

diff --git a/code_eval/OpenCodeEval/benchmark/HumanEval.py b/code_eval/OpenCodeEval/benchmark/HumanEval.py
new file mode 100644
index 0000000..3c3aece
--- /dev/null
+++ b/code_eval/OpenCodeEval/benchmark/HumanEval.py
@@ -0,0 +1,114 @@
+import os
+from typing import Literal
+
+from OpenCodeEval.benchmark.base import Benchmark, PYTHON_STOP, PYTHON_IMPORTS
+from OpenCodeEval.utils import refine_text, stream_jsonl, program_extract
+from OpenCodeEval.eval.func_eval import check_correctness
+from OpenCodeEval.eval.sanitize import sanitize
+
+class HumanEval(Benchmark):
+
+    name: str = "HumanEval"
+
+    imports_code = PYTHON_IMPORTS
+    chat_stop = PYTHON_STOP
+    base_stop = ["\ndef ", "\nclass ", "\nimport ", "\nfrom ", "\nassert "]
+
+    def __init__(
+        self,
+        split: Literal["base", "hard"] = "base",
+        time_out: float = 3.0,
+        prompt_type: str = "Completion"
+    ):
+
+        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}.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["task_id"].split("/")[-1])
+            
+            tasks[task_id] = task_data
+        
+        return tasks
+
+    def get_prompt(self):
+        """
+        Builds the prompt for the LM to generate from.
+        """
+
+        assert self.prompt_type == "Completion", "Prompt type must be Completion for HumanEval"
+
+        prompts = []
+        for task_id, task_data in self.tasks.items():
+            prompts.append(
+                dict(
+                    task_id = task_id,
+                    prompt = refine_text(task_data['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()[-200:])
+
+            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 = (
+            "\n".join(self.imports_code) + "\n"
+            + task_data["prompt"] + "\n"
+            + "    pass\n" + "\n"
+            + solution['solution'] + "\n"
+            + task_data['test'] + "\n"
+            + f"check({task_data['entry_point']})"
+            )
+
+        result = check_correctness(
+            solution['task_id'],
+            solution['completion_id'],
+            code,
+            self.time_out
+        )
+        
+        return result
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