utils.py

import re
import bittensor as bt
import time
import json
from aiohttp import web
from collections import Counter
from prompting.rewards import DateRewardModel, FloatDiffModel

UNSUCCESSFUL_RESPONSE_PATTERNS = ["I'm sorry", "unable to", "I cannot", "I can't", "I am unable", "I am sorry", "I can not", "don't know", "not sure", "don't understand", "not capable"]

reward_models = {
    'date_qa': DateRewardModel(),
    'math': FloatDiffModel(),
}

def completion_is_valid(completion: str):
    """
    Get the completion statuses from the completions.
    """
    if not completion.strip():
        return False

    patt = re.compile(r'\b(?:' + '|'.join(UNSUCCESSFUL_RESPONSE_PATTERNS) + r')\b', re.IGNORECASE)
    if not len(re.findall(r'\w+',completion)) or patt.search(completion):
        return False
    return True


def ensemble_result(completions: list, task_name: str, prefer: str = 'longest'):
    """
    Ensemble completions from multiple models.
    # TODO: Measure agreement
    # TODO: Figure out how to mitigate the cabal effect (large groups will appear to be more credible)
    # TODO: Reward pipeline
    """
    if not completions:
        return None

    answer = None
    if task_name in ('qa', 'summarization'):
        # No special handling for QA or summarization
        supporting_completions = completions

    elif task_name == 'date_qa':
        # filter the completions to be the ones that contain valid dates and if there are multiple dates, select the most common one (with support > 1)
        dates = list(map(reward_models[task_name].parse_dates_from_text, completions))
        bt.logging.info(f"Unprocessed dates: {dates}")
        valid_date_indices = [i for i, d in enumerate(dates) if d]
        valid_completions = [completions[i] for i in valid_date_indices]
        valid_dates = [dates[i] for i in valid_date_indices]
        dates = [f"{d[0].strftime('%-d %B')} {d[1]}" for d in valid_dates]
        if not dates:
            return None

        counter = Counter(dates)
        most_common, count = counter.most_common()[0]
        answer = most_common
        if count == 1:
            supporting_completions = valid_completions
        else:
            supporting_completions = [c for i, c in enumerate(valid_completions) if dates[i]==most_common]

    elif task_name == 'math':
        # filter the completions to be the ones that contain valid numbers and if there are multiple values, select the most common one (with support > 1)
        # TODO: use the median instead of the most common value
        vals = list(map(reward_models[task_name].extract_number, completions))
        vals = [val for val in vals if val]
        if not vals:
            return None

        most_common, count = Counter(dates).most_common()[0]
        bt.logging.info(f"Most common value: {most_common}, count: {count}")
        answer = most_common
        if count == 1:
            supporting_completions = completions
        else:
            supporting_completions = [c for i, c in enumerate(completions) if vals[i]==most_common]


    bt.logging.info(f"Supporting completions: {supporting_completions}")
    if prefer == 'longest':
        preferred_completion = sorted(supporting_completions, key=len)[-1]
    elif prefer == 'shortest':
        preferred_completion = sorted(supporting_completions, key=len)[0]
    elif prefer == 'most_common':
        preferred_completion = max(set(supporting_completions), key=supporting_completions.count)
    else:
        raise ValueError(f"Unknown ensemble preference: {prefer}")

    return {
        'completion': preferred_completion,
        'accepted_answer': answer,
        'support': len(supporting_completions),
        'support_indices': [completions.index(c) for c in supporting_completions],
        'method': f'Selected the {prefer.replace("_", " ")} completion'
    }

def guess_task_name(challenge: str):
    # TODO: use a pre-trained classifier to guess the task name
    categories = {
        'summarization': re.compile('summar|quick rundown|overview'),
        'date_qa': re.compile('exact date|tell me when|on what date|on what day|was born?|died?'),
        'math': re.compile('math|solve|solution| sum |problem|geometric|vector|calculate|degrees|decimal|factorial'),
    }
    for task_name, patt in categories.items():
        if patt.search(challenge):
            return task_name

    return 'qa'


async def echo_stream(request_data: dict):
    k = request_data.get('k', 1)
    exclude = request_data.get('exclude', [])
    timeout = request_data.get('timeout', 0.2)
    message = '\n\n'.join(request_data['messages'])

    # Create a StreamResponse
    response = web.StreamResponse(status=200, reason='OK', headers={'Content-Type': 'text/plain'})
    await response.prepare()

    completion = ''
    # Echo the message k times with a timeout between each chunk
    for _ in range(k):
        for word in message.split():
            chunk = f'{word} '
            await response.write(chunk.encode('utf-8'))
            completion += chunk
            time.sleep(timeout)
            bt.logging.info(f"Echoed: {chunk}")

    completion = completion.strip()

    # Prepare final JSON chunk
    json_chunk = json.dumps({
        "uids": [0],
        "completion": completion,
        "completions": [completion.strip()],
        "timings": [0],
        "status_messages": ['Went well!'],
        "status_codes": [200],
        "completion_is_valid": [True],
        "task_name": 'echo',
        "ensemble_result": {}
    })

    # Send the final JSON as part of the stream
    await response.write(f"\n\nJSON_RESPONSE_BEGIN:\n{json_chunk}".encode('utf-8'))

    # Finalize the response
    await response.write_eof()
    return response