# coding=utf-8
import random
import logging
import time
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence

def sequence_padding(inputs, length=None, value=0, seq_dims=1, mode='post'):
    """将序列padding到同一长度
    """
    # global inp
    # if len(list(inputs))==0:
    #     inputs=inp
    if isinstance(inputs[0], (np.ndarray, list)):
        # inp=inputs
        if length is None:
            length = np.max([np.shape(x)[:seq_dims] for x in inputs], axis=0)
        elif not hasattr(length, '__getitem__'):
            length = [length]

        slices = [np.s_[:length[i]] for i in range(seq_dims)]
        slices = tuple(slices) if len(slices) > 1 else slices[0]
        pad_width = [(0, 0) for _ in np.shape(inputs[0])]

        outputs = []
        for x in inputs:
            x = x[slices]
            for i in range(seq_dims):
                if mode == 'post':
                    pad_width[i] = (0, length[i] - np.shape(x)[i])
                elif mode == 'pre':
                    pad_width[i] = (length[i] - np.shape(x)[i], 0)
                else:
                    raise ValueError('"mode" argument must be "post" or "pre".')
            x = np.pad(x, pad_width, 'constant', constant_values=value)
            outputs.append(x)

        return np.array(outputs)
    
    elif isinstance(inputs[0], torch.Tensor):

        # assert mode == 'post', '"mode" argument must be "post" when element is torch.Tensor'
        if length is not None:
            inputs = [i[:length] for i in inputs]
        return pad_sequence(inputs, padding_value=value, batch_first=True)
    elif len(list(inputs))==0:
        a=1
    else:
    # else:
            a=1
        # raise ValueError('"input" argument must be tensor/list/ndarray.')

def timer(func):
    """
    函数计时器
    :param func:
    :return:
    """

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        res = func(*args, **kwargs)
        end = time.time()
        print("{}共耗时约{:.4f}秒".format(func.__name__, end - start))
        return res

    return wrapper


def set_seed(seed=123):
    """
    设置随机数种子，保证实验可重现
    :param seed:
    :return:
    """
    random.seed(seed)
    torch.manual_seed(seed)
    np.random.seed(seed)
    torch.cuda.manual_seed_all(seed)


def set_logger(log_path):
    """
    配置log
    :param log_path:s
    :return:
    """
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    # 由于每调用一次set_logger函数，就会创建一个handler，会造成重复打印的问题，因此需要判断root logger中是否已有该handler
    if not any(handler.__class__ == logging.FileHandler for handler in logger.handlers):
        file_handler = logging.FileHandler(log_path)
        formatter = logging.Formatter(
            '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)d - %(message)s')
        file_handler.setFormatter(formatter)
        logger.addHandler(file_handler)

    if not any(handler.__class__ == logging.StreamHandler for handler in logger.handlers):
        stream_handler = logging.StreamHandler()
        stream_handler.setFormatter(logging.Formatter('%(message)s'))
        logger.addHandler(stream_handler)