"""
util tools
"""
from __future__ import print_function
import os
import sys
import numpy as np
import paddle.fluid as fluid


def str2bool(v):
    """
    argparse does not support True or False in python
    """
    return v.lower() in ("true", "t", "1")


class ArgumentGroup(object):
    """
    Put arguments to one group
    """
    def __init__(self, parser, title, des):
        """none"""
        self._group = parser.add_argument_group(title=title, description=des)

    def add_arg(self, name, type, default, help, **kwargs):
        """ Add argument """
        type = str2bool if type == bool else type
        self._group.add_argument(
            "--" + name,
            default=default,
            type=type,
            help=help + ' Default: %(default)s.',
            **kwargs)


def print_arguments(args):
    """none"""
    print('-----------  Configuration Arguments -----------')
    for arg, value in sorted(vars(args).items()):
        print('%s: %s' % (arg, value))
    print('------------------------------------------------')


def to_str(string, encoding="utf-8"):
    """convert to str for print"""
    if sys.version_info.major == 3:
        if isinstance(string, bytes):
            return string.decode(encoding)
    elif sys.version_info.major == 2:
        if isinstance(string, unicode):
            return string.encode(encoding)
    return string


def to_lodtensor(data, place):
    """
    Convert data in list into lodtensor.
    """
    seq_lens = [len(seq) for seq in data]
    cur_len = 0
    lod = [cur_len]
    for l in seq_lens:
        cur_len += l
        lod.append(cur_len)
    flattened_data = np.concatenate(data, axis=0).astype("int64")
    flattened_data = flattened_data.reshape([len(flattened_data), 1])
    res = fluid.LoDTensor()
    res.set(flattened_data, place)
    res.set_lod([lod])
    return res


def parse_result(words, crf_decode, dataset):
    """ parse result """
    offset_list = (crf_decode.lod())[0]
    words = np.array(words)
    crf_decode = np.array(crf_decode)
    batch_size = len(offset_list) - 1
    batch_out_str = []
    for sent_index in range(batch_size):
        sent_out_str = ""
        sent_len = offset_list[sent_index + 1] - offset_list[sent_index]
        for tag_index in range(sent_len): # iterate every word in sent
            index = tag_index + offset_list[sent_index]
            cur_word_id = str(words[index][0])
            cur_tag_id = str(crf_decode[index][0])
            cur_word = dataset.id2word_dict[cur_word_id]
            cur_tag = dataset.id2label_dict[cur_tag_id]
            sent_out_str += cur_word + u"/" + cur_tag + u" "
        sent_out_str = to_str(sent_out_str.strip())
        batch_out_str.append(sent_out_str)
    return batch_out_str


def init_checkpoint(exe, init_checkpoint_path, main_program):
    """
    Init CheckPoint
    """
    assert os.path.exists(
        init_checkpoint_path), "[%s] cann't be found." % init_checkpoint_path

    def existed_persitables(var):
        """
        If existed presitabels
        """
        if not fluid.io.is_persistable(var):
            return False
        return os.path.exists(os.path.join(init_checkpoint_path, var.name))

    fluid.io.load_vars(
        exe,
        init_checkpoint_path,
        main_program=main_program,
        predicate=existed_persitables)
    print("Load model from {}".format(init_checkpoint_path))


def init_pretraining_params(exe,
                            pretraining_params_path,
                            main_program,
                            use_fp16=False):
    """load params of pretrained model, NOT including moment, learning_rate"""
    assert os.path.exists(pretraining_params_path
                          ), "[%s] cann't be found." % pretraining_params_path

    def _existed_params(var):
        if not isinstance(var, fluid.framework.Parameter):
            return False
        return os.path.exists(os.path.join(pretraining_params_path, var.name))

    fluid.io.load_vars(
        exe,
        pretraining_params_path,
        main_program=main_program,
        predicate=_existed_params)
    print("Load pretraining parameters from {}.".format(
        pretraining_params_path))