"""Normalize assistant reasoning before sending history to an inference server.

The tested vLLM response uses `reasoning`; the tested llama-server input parser
accepts `reasoning_content`. This helper preserves content and tool-call fields.
"""
from copy import deepcopy

def normalize_messages(messages):
    result = deepcopy(messages)
    for message in result:
        if message.get('role') != 'assistant':
            continue
        alias = message.get('reasoning')
        if isinstance(alias, str):
            if not message.get('reasoning_content'):
                message['reasoning_content'] = alias
            del message['reasoning']
        content = message.get('content')
        if isinstance(content, str) and content.startswith('<think>') and '</think>' in content:
            thought, answer = content.split('</think>', 1)
            if not message.get('reasoning_content'):
                message['reasoning_content'] = thought[7:].strip()
            message['content'] = answer.strip()
    return result

if __name__ == '__main__':
    import json, sys
    json.dump(normalize_messages(json.load(sys.stdin)), sys.stdout, ensure_ascii=False, indent=2)
    sys.stdout.write('\n')