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78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
# Copyright Unakar
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# Modified from https://github.com/Unakar/Logic-RL/blob/086373176ac198c97277ff50f4b6e7e1bfe669d3/verl/utils/reward_score/kk.py#L99
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Dict, Optional, Tuple
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def validate_response_structure(processed_str: str, tags: Dict = None) -> bool:
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"""Performs comprehensive validation of response structure.
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Args:
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processed_str: Processed response string from the model
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Returns:
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Boolean indicating whether all formatting requirements are met
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"""
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validation_passed = True
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# Check required tags
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if tags is None:
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tags = {
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"think_start": {"text": "<think>", "num_occur": 1},
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"think_end": {"text": "</think>", "num_occur": 1},
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"answer_start": {"text": "<answer>", "num_occur": 1},
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"answer_end": {"text": "</answer>", "num_occur": 1},
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}
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positions = {}
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for tag_name, tag_info in tags.items():
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tag_str = tag_info["text"]
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expected_count = tag_info["num_occur"]
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count = processed_str.count(tag_str)
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positions[tag_name] = pos = processed_str.find(tag_str)
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if count != expected_count:
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validation_passed = False
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# Verify tag order
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if (
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positions["think_start"] > positions["think_end"]
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or positions["think_end"] > positions["answer_start"]
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or positions["answer_start"] > positions["answer_end"]
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):
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validation_passed = False
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if len(processed_str) - positions["answer_end"] != len(tags["answer_end"]["text"]):
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validation_passed = False
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return validation_passed
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def extract_solution(solution_str: str) -> Tuple[Optional[str], str]:
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"""Extracts the final answer from the model's response string.
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Args:
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solution_str: Raw response string from the language model
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Returns:
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Tuple containing (extracted_answer, processed_string)
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"""
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# Extract final answer using XML-style tags
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# answer_pattern = r"<answer>(.*?)</answer>"
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answer_pattern = r"boxed{(.*?)}"
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matches = list(re.finditer(answer_pattern, solution_str, re.DOTALL))
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if not matches:
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return None, solution_str
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final_answer = matches[-1].group(1).strip()
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return final_answer, solution_str
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