import pandas as pd
from typing import Optional

def launch_wandb(project_name:str, default_config:dict, sweep:bool=False):
    from wandb_setup import get_wandb
    wandb = get_wandb()   
    
    if wandb is None: 
        return None
    elif sweep:
        wandb.init(project=project_name,  config = default_config)             
        return wandb
    else:
        wandb.init(project=project_name, config = default_config, reinit=True)
        return wandb
    
    
def seperate_configs(wandb: Optional[object], model_config:dict, training_config:dict, data_config:dict) -> tuple[dict,dict,dict]:
    config: dict = wandb.config

    if type(wandb) is not type(None):
        for k in training_config: training_config[k] = config[k]
        for k in model_config: model_config[k] = config[k]
        # for k in data_config: data_config[k] = config[k]

    return model_config, training_config, data_config


def send_report_to_wandb(results: pd.DataFrame, wandb:Optional[object], project_name: str, model_name: str):
    if wandb is None: return

    run = wandb.init(project=project_name, config={"model_type": model_name}, reinit=True)
    wandb.run.name = model_name+ "-" + wandb.run.id
    wandb.run.save()

    mean_results = results.mean(axis = 1)
    for key, value in mean_results.iteritems():
        run.log({"model_type": model_name, key: value })

    run.finish()


        