<gh_stars>0
from typing import Dict
from optparse import OptionParser

from .data_io import read_conf, str_to_bool


def load(config_file_path):
    options = read_conf(['--cfg', config_file_path])

    parser = OptionParser()
    cfg, _ = parser.parse_args([])

    # [data]
    cfg.tr_lst = options.tr_lst
    cfg.te_lst = options.te_lst
    cfg.pt_file = options.pt_file
    cfg.class_dict_file = options.lab_dict
    cfg.data_folder = options.data_folder.rstrip('/') + '/'
    cfg.output_folder = options.output_folder.rstrip('/') + '/'

    # [windowing]
    cfg.fs = int(options.fs)
    cfg.cw_len_ms = int(options.cw_len_ms)
    cfg.cw_shift_ms = int(options.cw_shift_ms)

    # [cnn]
    cfg.cnn_N_filter = list(map(int, options.cnn_N_filter.split(',')))
    cfg.cnn_len_filter = list(map(int, options.cnn_len_filter.split(',')))
    cfg.cnn_max_pool_len = list(map(int, options.cnn_max_pool_len.split(',')))
    cfg.cnn_use_layer_norm_inp = str_to_bool(options.cnn_use_layer_norm_inp)
    cfg.cnn_use_batch_norm_inp = str_to_bool(options.cnn_use_batch_norm_inp)
    cfg.cnn_use_layer_norm = list(map(str_to_bool, options.cnn_use_layer_norm.split(',')))
    cfg.cnn_use_batch_norm = list(map(str_to_bool, options.cnn_use_batch_norm.split(',')))
    cfg.cnn_act = list(map(str, options.cnn_act.split(',')))
    cfg.cnn_drop = list(map(float, options.cnn_drop.split(',')))

    # [dnn]
    cfg.fc_lay = list(map(int, options.fc_lay.split(',')))
    cfg.fc_drop = list(map(float, options.fc_drop.split(',')))
    cfg.fc_use_layer_norm_inp = str_to_bool(options.fc_use_layer_norm_inp)
    cfg.fc_use_batch_norm_inp = str_to_bool(options.fc_use_batch_norm_inp)
    cfg.fc_use_batch_norm = list(map(str_to_bool, options.fc_use_batch_norm.split(',')))
    cfg.fc_use_layer_norm = list(map(str_to_bool, options.fc_use_layer_norm.split(',')))
    cfg.fc_act = list(map(str, options.fc_act.split(',')))

    # [class]
    cfg.class_lay = list(map(int, options.class_lay.split(',')))
    cfg.class_drop = list(map(float, options.class_drop.split(',')))
    cfg.class_use_layer_norm_inp = str_to_bool(options.class_use_layer_norm_inp)
    cfg.class_use_batch_norm_inp = str_to_bool(options.class_use_batch_norm_inp)
    cfg.class_use_batch_norm = list(
        map(str_to_bool, options.class_use_batch_norm.split(','))
    )
    cfg.class_use_layer_norm = list(map(str_to_bool, options.class_use_layer_norm.split(',')))
    cfg.class_act = list(map(str, options.class_act.split(',')))

    # [optimization]
    cfg.lr = float(options.lr)
    cfg.batch_size = int(options.batch_size)
    cfg.N_epochs = int(options.N_epochs)
    cfg.N_batches = int(options.N_batches)
    cfg.N_eval_epoch = int(options.N_eval_epoch)
    cfg.seed = int(options.seed)

    return cfg

def make_sinc_net_init_options(config) -> Dict:
    options = config

    fs = options.fs
    cw_len_ms = options.cw_len_ms

    cnn_N_filter = options.cnn_N_filter
    cnn_len_filter = options.cnn_len_filter
    cnn_max_pool_len = options.cnn_max_pool_len
    cnn_use_layer_norm_inp = options.cnn_use_layer_norm_inp
    cnn_use_batch_norm_inp = options.cnn_use_batch_norm_inp
    cnn_use_layer_norm = options.cnn_use_layer_norm
    cnn_use_batch_norm = options.cnn_use_batch_norm
    cnn_act = options.cnn_act
    cnn_drop = options.cnn_drop

    # converting context and shift in samples
    wlen = int(fs * cw_len_ms / 1000.00)

    init_arg = {
        'input_dim': wlen,
        'fs': fs,
        'cnn_N_filter': cnn_N_filter,
        'cnn_len_filter': cnn_len_filter,
        'cnn_max_pool_len': cnn_max_pool_len,
        'cnn_use_layer_norm_inp': cnn_use_layer_norm_inp,
        'cnn_use_batch_norm_inp': cnn_use_batch_norm_inp,
        'cnn_use_layer_norm': cnn_use_layer_norm,
        'cnn_use_batch_norm': cnn_use_batch_norm,
        'cnn_act': cnn_act,
        'cnn_drop': cnn_drop,
    }
    
    return init_arg
from typing import Dict
from optparse import OptionParser

from .data_io import read_conf, str_to_bool


def load(config_file_path):
    options = read_conf(['--cfg', config_file_path])

    parser = OptionParser()
    cfg, _ = parser.parse_args([])

    # [data]
    cfg.tr_lst = options.tr_lst
    cfg.te_lst = options.te_lst
    cfg.pt_file = options.pt_file
    cfg.class_dict_file = options.lab_dict
    cfg.data_folder = options.data_folder.rstrip('/') + '/'
    cfg.output_folder = options.output_folder.rstrip('/') + '/'

    # [windowing]
    cfg.fs = int(options.fs)
    cfg.cw_len_ms = int(options.cw_len_ms)
    cfg.cw_shift_ms = int(options.cw_shift_ms)

    # [cnn]
    cfg.cnn_N_filter = list(map(int, options.cnn_N_filter.split(',')))
    cfg.cnn_len_filter = list(map(int, options.cnn_len_filter.split(',')))
    cfg.cnn_max_pool_len = list(map(int, options.cnn_max_pool_len.split(',')))
    cfg.cnn_use_layer_norm_inp = str_to_bool(options.cnn_use_layer_norm_inp)
    cfg.cnn_use_batch_norm_inp = str_to_bool(options.cnn_use_batch_norm_inp)
    cfg.cnn_use_layer_norm = list(map(str_to_bool, options.cnn_use_layer_norm.split(',')))
    cfg.cnn_use_batch_norm = list(map(str_to_bool, options.cnn_use_batch_norm.split(',')))
    cfg.cnn_act = list(map(str, options.cnn_act.split(',')))
    cfg.cnn_drop = list(map(float, options.cnn_drop.split(',')))

    # [dnn]
    cfg.fc_lay = list(map(int, options.fc_lay.split(',')))
    cfg.fc_drop = list(map(float, options.fc_drop.split(',')))
    cfg.fc_use_layer_norm_inp = str_to_bool(options.fc_use_layer_norm_inp)
    cfg.fc_use_batch_norm_inp = str_to_bool(options.fc_use_batch_norm_inp)
    cfg.fc_use_batch_norm = list(map(str_to_bool, options.fc_use_batch_norm.split(',')))
    cfg.fc_use_layer_norm = list(map(str_to_bool, options.fc_use_layer_norm.split(',')))
    cfg.fc_act = list(map(str, options.fc_act.split(',')))

    # [class]
    cfg.class_lay = list(map(int, options.class_lay.split(',')))
    cfg.class_drop = list(map(float, options.class_drop.split(',')))
    cfg.class_use_layer_norm_inp = str_to_bool(options.class_use_layer_norm_inp)
    cfg.class_use_batch_norm_inp = str_to_bool(options.class_use_batch_norm_inp)
    cfg.class_use_batch_norm = list(
        map(str_to_bool, options.class_use_batch_norm.split(','))
    )
    cfg.class_use_layer_norm = list(map(str_to_bool, options.class_use_layer_norm.split(',')))
    cfg.class_act = list(map(str, options.class_act.split(',')))

    # [optimization]
    cfg.lr = float(options.lr)
    cfg.batch_size = int(options.batch_size)
    cfg.N_epochs = int(options.N_epochs)
    cfg.N_batches = int(options.N_batches)
    cfg.N_eval_epoch = int(options.N_eval_epoch)
    cfg.seed = int(options.seed)

    return cfg

def make_sinc_net_init_options(config) -> Dict:
    options = config

    fs = options.fs
    cw_len_ms = options.cw_len_ms

    cnn_N_filter = options.cnn_N_filter
    cnn_len_filter = options.cnn_len_filter
    cnn_max_pool_len = options.cnn_max_pool_len
    cnn_use_layer_norm_inp = options.cnn_use_layer_norm_inp
    cnn_use_batch_norm_inp = options.cnn_use_batch_norm_inp
    cnn_use_layer_norm = options.cnn_use_layer_norm
    cnn_use_batch_norm = options.cnn_use_batch_norm
    cnn_act = options.cnn_act
    cnn_drop = options.cnn_drop

    # converting context and shift in samples
    wlen = int(fs * cw_len_ms / 1000.00)

    init_arg = {
        'input_dim': wlen,
        'fs': fs,
        'cnn_N_filter': cnn_N_filter,
        'cnn_len_filter': cnn_len_filter,
        'cnn_max_pool_len': cnn_max_pool_len,
        'cnn_use_layer_norm_inp': cnn_use_layer_norm_inp,
        'cnn_use_batch_norm_inp': cnn_use_batch_norm_inp,
        'cnn_use_layer_norm': cnn_use_layer_norm,
        'cnn_use_batch_norm': cnn_use_batch_norm,
        'cnn_act': cnn_act,
        'cnn_drop': cnn_drop,
    }
    
    return init_arg
# [data] # [windowing] # [cnn] # [dnn] # [class] # [optimization] # converting context and shift in samples