# coding: utf-8
import os
import numpy as np
import time

# Select the best-resolution timer function
try:
    _get_time = time.perf_counter
except AttributeError:
    if os.name == 'nt':
        _get_time = time.clock
    else:
        _get_time = time.time


def elapsed_time(_done, _start_at, _final_elapsed_time, _snapshot_elapsed_time):
    """Total time used for the training.

    The time is in seconds. If the training is resumed from snapshot, it
    includes the time of all the previous training to get the current
    state of the trainer.

    """
    if _done:
        return _final_elapsed_time
    if _start_at is None:
        raise RuntimeError('training has not been started yet')
    return _get_time() - _start_at + _snapshot_elapsed_time


def smooth_curve(x):
    """損失関数のグラフを滑らかにするために用いる

    参考：http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
    """
    window_len = 11
    s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
    w = np.kaiser(window_len, 2)
    y = np.convolve(w/w.sum(), s, mode='valid')
    return y[5:len(y)-5]


def shuffle_dataset(x, t):
    """データセットのシャッフルを行う

    Parameters
    ----------
    x : 訓練データ
    t : 教師データ

    Returns
    -------
    x, t : シャッフルを行った訓練データと教師データ
    """
    permutation = np.random.permutation(x.shape[0])
    x = x[permutation,:] if x.ndim == 2 else x[permutation,:,:,:]
    t = t[permutation]

    return x, t

def conv_output_size(input_size, filter_size, stride=1, pad=0):
    return (input_size + 2*pad - filter_size) / stride + 1


def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
    """

    Parameters
    ----------
    input_data : (データ数, チャンネル, 高さ, 幅)の4次元配列からなる入力データ
    filter_h : フィルターの高さ
    filter_w : フィルターの幅
    stride : ストライド
    pad : パディング

    Returns
    -------
    col : 2次元配列
    """
    N, C, H, W = input_data.shape
    out_h = (H + 2*pad - filter_h)//stride + 1
    out_w = (W + 2*pad - filter_w)//stride + 1

    img = np.pad(input_data, [(0,0), (0,0), (pad, pad), (pad, pad)], 'constant')
    col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))

    for y in range(filter_h):
        y_max = y + stride*out_h
        for x in range(filter_w):
            x_max = x + stride*out_w
            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]

    col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N*out_h*out_w, -1)
    return col


def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
    """

    Parameters
    ----------
    col :
    input_shape : 入力データの形状（例：(10, 1, 28, 28)）
    filter_h :
    filter_w
    stride
    pad

    Returns
    -------

    """
    N, C, H, W = input_shape
    out_h = (H + 2*pad - filter_h)//stride + 1
    out_w = (W + 2*pad - filter_w)//stride + 1
    col = col.reshape(N, out_h, out_w, C, filter_h, filter_w).transpose(0, 3, 4, 5, 1, 2)

    img = np.zeros((N, C, H + 2*pad + stride - 1, W + 2*pad + stride - 1))
    for y in range(filter_h):
        y_max = y + stride*out_h
        for x in range(filter_w):
            x_max = x + stride*out_w
            img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]

    return img[:, :, pad:H + pad, pad:W + pad]