Delete utils.py

utils.py

@@ -1,126 +0,0 @@
-import logging
-import re
-from typing import Union
-
-import datasets
-import evaluate as hf_evaluate
-
-
-eval_logger = logging.getLogger(__name__)
-
-
-try:
-    pass_at_k = hf_evaluate.load("code_eval")
-
-    # run simple test to check code execution is enabled before model generation
-    test_cases = ["assert add(2, 3)==5"]
-    candidates = [["def add(a,b): return a*b"]]
-    results = pass_at_k.compute(references=test_cases, predictions=candidates, k=[1])
-except Exception as e:
-    raise e
-
-
-def load_dataset(**kwargs):
-    """Load MBPP long-context dataset with specified context length."""
-    context_length = kwargs.get("context_length", "128k")
-
-    eval_logger.info(
-        f"Loading mbpp_longcontext dataset: context_length={context_length}"
-    )
-    dataset = datasets.load_dataset("jannalu/mbpp-longcontext", name=context_length)
-    return dataset
-
-
-def pass_at_1(
-    references: Union[str, list[str]], predictions: Union[str, list[list[str]]]
-) -> float:
-    """Compute pass@1 metric for code generation."""
-    if isinstance(references, str):
-        references = [references]
-    if isinstance(predictions[0], str):
-        predictions = [[p] for p in predictions]
-    return pass_at_k.compute(
-        references=references,
-        predictions=predictions,
-        k=[1],
-    )[0]["pass@1"]
-
-
-def extract_code_blocks(text: str) -> str:
-    """Extract code from markdown code blocks in generated text."""
-    # Pattern to match ```...``` blocks
-    pattern = r"```(?:\w+)?\n?(.*?)\n?```"
-    # (+ ```) as we add the opening "```python" to the gen_prefix
-    matches = re.findall(pattern, r"```" + text, re.DOTALL)
-    # if no matches, try to match ```...``` blocks (after removing the language)
-    if not matches:
-        text_without_lang = re.sub(r"```python", "```", text)
-        matches = re.findall(pattern, text_without_lang, re.DOTALL)
-    if not matches:
-        return ""
-    else:
-        return matches[0]
-
-
-def build_predictions(resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
-    """Build predictions by extracting code blocks from model responses."""
-    return [[extract_code_blocks(r) for r in resp] for resp in resps]
-
-
-def doc_to_metadata(doc: dict) -> dict:
-    """
-    Extract metadata from a document for tracking and analysis.
-
-    This extracts the context_length_tokens field so results can be
-    grouped and analyzed by sequence length.
-    """
-    return {
-        "seq_length": doc.get("context_length_tokens", 0),
-        "context_id": doc.get("context_id", ""),
-        "context_type": doc.get("context_type", "narrative"),
-    }
-
-
-def list_fewshot_samples():
-    """
-    Return few-shot examples for MBPP long-context.
-    Note: These examples do NOT include the long context since they're used for few-shot.
-    """
-    return [
-        {
-            "task_id": 2,
-            "text": "Write a function to find the similar elements from the given two tuple lists.",
-            "code": "def similar_elements(test_tup1, test_tup2):\r\n  res = tuple(set(test_tup1) & set(test_tup2))\r\n  return (res) ",
-            "test_list": [
-                "assert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)",
-                "assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4)",
-                "assert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14)",
-            ],
-            "is_fewshot": True,
-            "prompt_with_context": "Here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\n\nassert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)\nassert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4)\nassert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14)\n[BEGIN]\n",
-        },
-        {
-            "task_id": 3,
-            "text": "Write a python function to identify non-prime numbers.",
-            "code": "import math\r\ndef is_not_prime(n):\r\n    result = False\r\n    for i in range(2,int(math.sqrt(n)) + 1):\r\n        if n % i == 0:\r\n            result = True\r\n    return result",
-            "test_list": [
-                "assert is_not_prime(2) == False",
-                "assert is_not_prime(10) == True",
-                "assert is_not_prime(35) == True",
-            ],
-            "is_fewshot": True,
-            "prompt_with_context": "Here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\nassert is_not_prime(2) == False\nassert is_not_prime(10) == True\nassert is_not_prime(35) == True\n[BEGIN]\n",
-        },
-        {
-            "task_id": 4,
-            "text": "Write a function to find the largest integers from a given list of numbers using heap queue algorithm.",
-            "code": "import heapq as hq\r\ndef heap_queue_largest(nums,n):\r\n  largest_nums = hq.nlargest(n, nums)\r\n  return largest_nums",
-            "test_list": [
-                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] ",
-                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] ",
-                "assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35]",
-            ],
-            "is_fewshot": True,
-            "prompt_with_context": "Here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\n\nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35]\n[BEGIN]\n",
-        },
-    ]