import time

import requests

from llm_server import opts
from llm_server.cluster.cluster_config import cluster_config, get_backends
from llm_server.custom_redis import redis
from llm_server.database.database import weighted_average_column_for_model
from llm_server.llm.info import get_info


def main_background_thread():
    while True:
        online, offline = get_backends()
        for backend_url in online:
            backend_info = cluster_config.get_backend(backend_url)
            backend_mode = backend_info['mode']
            backend_info = get_info(backend_url, backend_mode)
            running_model = backend_info.get('model')
            if not running_model:
                continue

            average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps = calc_stats_for_backend(backend_url, running_model, backend_mode)
            if average_generation_elapsed_sec:  # returns None on exception
                cluster_config.set_backend_value(backend_url, 'average_generation_elapsed_sec', average_generation_elapsed_sec)
            if average_output_tokens:
                cluster_config.set_backend_value(backend_url, 'average_output_tokens', average_output_tokens)
            if average_generation_elapsed_sec and average_output_tokens:
                cluster_config.set_backend_value(backend_url, 'estimated_avg_tps', estimated_avg_tps)

        if opts.background_homepage_cacher:
            try:
                base_client_api = redis.get('base_client_api', dtype=str)
                r = requests.get('https://' + base_client_api, timeout=5)
            except Exception as e:
                print(f'Failed fetch the homepage - {e.__class__.__name__}: {e}')

        time.sleep(30)


def calc_stats_for_backend(backend_url, running_model, backend_mode):
    # exclude_zeros=True filters out rows where an error message was returned. Previously, if there was an error, 0
    # was entered into the column. The new code enters null instead but we need to be backwards compatible for now.
    average_generation_elapsed_sec = weighted_average_column_for_model('prompts', 'generation_time',
                                                                       running_model, backend_mode, backend_url, exclude_zeros=True,
                                                                       include_system_tokens=opts.include_system_tokens_in_stats) or 0
    average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens',
                                                              running_model, backend_mode, backend_url, exclude_zeros=True,
                                                              include_system_tokens=opts.include_system_tokens_in_stats) or 0
    estimated_avg_tps = round(average_output_tokens / average_generation_elapsed_sec, 2) if average_generation_elapsed_sec > 0 else 0  # Avoid division by zero
    return average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps