import os
from argparse import Namespace
import re
from os.path import join as pjoin
from utils.word_vectorizer import POS_enumerator


def is_float(numStr):
    flag = False
    numStr = str(numStr).strip().lstrip('-').lstrip('+')
    try:
        reg = re.compile(r'^[-+]?[0-9]+\.[0-9]+$')
        res = reg.match(str(numStr))
        if res:
            flag = True
    except Exception as ex:
        print("is_float() - error: " + str(ex))
    return flag


def is_number(numStr):
    flag = False
    numStr = str(numStr).strip().lstrip('-').lstrip('+')
    if str(numStr).isdigit():
        flag = True
    return flag


def get_opt(opt_path, device):
    opt = Namespace()
    opt_dict = vars(opt)

    skip = ('-------------- End ----------------',
            '------------ Options -------------',
            '\n')
    print('Reading', opt_path)
    with open(opt_path) as f:
        for line in f:
            if line.strip() not in skip:
                # print(line.strip())
                key, value = line.strip().split(': ')
                if value in ('True', 'False'):
                    opt_dict[key] = True if value == 'True' else False
                elif is_float(value):
                    opt_dict[key] = float(value)
                elif is_number(value):
                    opt_dict[key] = int(value)
                else:
                    opt_dict[key] = str(value)

    opt_dict['which_epoch'] = 'latest'
    if 'num_layers' not in opt_dict:
        opt_dict['num_layers'] = 8
    if 'latent_dim' not in opt_dict:
        opt_dict['latent_dim'] = 512
    if 'diffusion_steps' not in opt_dict:
        opt_dict['diffusion_steps'] = 1000
    if 'no_clip' not in opt_dict:
        opt_dict['no_clip'] = False
    if 'no_eff' not in opt_dict:
        opt_dict['no_eff'] = False

    opt.save_root = pjoin(opt.checkpoints_dir, opt.dataset_name, opt.name)
    opt.model_dir = pjoin(opt.save_root, 'model')
    opt.meta_dir = pjoin(opt.save_root, 'meta')

    if opt.dataset_name == 't2m':
        opt.data_root = './data/HumanML3D'
        opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
        opt.text_dir = pjoin(opt.data_root, 'texts')
        opt.joints_num = 22
        opt.dim_pose = 263
        opt.max_motion_length = 196
    elif opt.dataset_name == 'kit':
        opt.data_root = './data/KIT-ML'
        opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs')
        opt.text_dir = pjoin(opt.data_root, 'texts')
        opt.joints_num = 21
        opt.dim_pose = 251
        opt.max_motion_length = 196
    else:
        raise KeyError('Dataset not recognized')

    opt.dim_word = 300
    opt.num_classes = 200 // opt.unit_length
    opt.dim_pos_ohot = len(POS_enumerator)
    opt.is_train = False
    opt.is_continue = False
    opt.device = device

    return opt