from .types import Config, RawConfig
from utils.helpers import flatten
from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
from models.model_map import get_model
from data_loader.collections import data_collections
from labeling.eventfilters_map import eventfilters_map
from labeling.labellers_map import labellers_map
from models.sklearn import SKLearnModel
from sklearn.ensemble import VotingClassifier

def preprocess_config(raw_config: RawConfig) -> Config:
    config_dict = vars(raw_config)
    config_dict = __preprocess_model_config(config_dict)
    config_dict = __preprocess_feature_extractors_config(config_dict)
    config_dict = __preprocess_data_collections_config(config_dict)
    config_dict = __preprocess_event_filter_config(config_dict)
    config_dict = __preprocess_event_labeller_config(config_dict)

    config_dict['no_of_classes'] = 'two'
    config_dict['mode'] = 'training'
    config = Config(**config_dict)
    return config

def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
    data_dict = data_dict.copy()
    keys = ['own_features', 'other_features', 'exogenous_features']
    for key in keys:
        preset_names = data_dict[key]
        data_dict[key] = flatten([feature_extractor_presets[preset_name] for preset_name in preset_names])
    return data_dict

def __preprocess_model_config(model_config: dict) -> dict:
    directional_models = [get_model(model_name) for model_name in model_config['directional_models']]
    model_config.pop('directional_models')
    model_config['directional_model'] = SKLearnModel(VotingClassifier([(m.name, m)for m in directional_models], voting ='soft'))
    if len(model_config['meta_models']) > 0:
        meta_models = [get_model(model_name) for model_name in model_config['meta_models']]
        model_config['meta_model'] = SKLearnModel(VotingClassifier([(m.name, m)for m in meta_models], voting ='soft'))
    model_config.pop('meta_models')

    return model_config

def __preprocess_data_collections_config(data_dict: dict) -> dict:
    keys = ['assets', 'other_assets', 'exogenous_data']
    for key in keys:
        preset_names = data_dict[key]
        data_dict[key] = flatten([data_collections[preset_name] for preset_name in preset_names])
    target_asset = next(iter([asset for asset in data_dict['assets'] if asset[1] == data_dict['target_asset']]), None)
    if target_asset is None: raise Exception('Target asset wasnt found in assets')
    data_dict['target_asset'] = target_asset
    return data_dict

def __preprocess_event_filter_config(data_dict: dict) -> dict:
    data_dict['event_filter'] = eventfilters_map[data_dict['event_filter']]
    return data_dict

def __preprocess_event_labeller_config(config_dict: dict) -> dict:
    config_dict['labeling'] = labellers_map[config_dict['labeling']](config_dict['forecasting_horizon'])
    return config_dict