# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/11/10 13:00
@Usage : 
@Desc :  构建一些即插即用的channelAttention
'''

import torch.nn as nn
import math
import numpy as np
import torch
import torch_dct as dct

try:
    from torch import irfft
    from torch import rfft
except ImportError:
    def rfft(x, d):
        t = torch.fft.fft(x, dim=(-d))
        r = torch.stack((t.real, t.imag), -1)
        return r


    def irfft(x, d):
        t = torch.fft.ifft(torch.complex(x[:, :, 0], x[:, :, 1]), dim=(-d))
        return t.real


# def dct(x, norm=None):
#     """
#     Discrete Cosine Transform, Type II (a.k.a. the DCT)
#
#     For the meaning of the parameter `norm`, see:
#     https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html
#
#     :param x: the input signal
#     :param norm: the normalization, None or 'ortho'
#     :return: the DCT-II of the signal over the last dimension
#     """
#     x_shape = x.shape
#     N = x_shape[-1]
#     x = x.contiguous().view(-1, N)
#
#     v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
#
#     # Vc = torch.fft.rfft(v, 1, onesided=False)
#     Vc = rfft(v, 1)
#
#     k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
#     W_r = torch.cos(k)
#     W_i = torch.sin(k)
#
#     V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i
#
#     if norm == 'ortho':
#         V[:, 0] /= np.sqrt(N) * 2
#         V[:, 1:] /= np.sqrt(N / 2) * 2
#
#     V = 2 * V.view(*x_shape)
#
#     return V

class dct_channel_block(nn.Module):
    def __init__(self, channel):
        super(dct_channel_block, self).__init__()
        # self.avg_pool = nn.AdaptiveAvgPool1d(1) #innovation
        self.fc = nn.Sequential(
            nn.Linear(channel, channel * 2, bias=False),
            nn.Dropout(p=0.1),
            nn.ReLU(inplace=True),
            nn.Linear(channel * 2, channel, bias=False),
            nn.Sigmoid()
        )
        # self.dct_norm = nn.LayerNorm([512], eps=1e-6)

        self.dct_norm = nn.LayerNorm([channel], eps=1e-6)  # for lstm on length-wise
        # self.dct_norm = nn.LayerNorm([36], eps=1e-6)#for lstm on length-wise on ill with input =36

    def forward(self, x):
        b, c = x.size()  # (B,C,L) (32,96,512)

        # list = []
        # for i in range(c):
        #     freq = dct.dct(x[:, :, i])
        #     list.append(freq)
        #
        # stack_dct = torch.stack(list, dim=2)

        # change = x.transpose(2, 1)
        stack_dct = dct.dct(x,norm='ortho')
        # stack_dct = stack_dct.transpose(2, 1)

        # stack_dct = torch.tensor(stack_dct)
        '''
        for traffic mission:f_weight = self.dct_norm(f_weight.permute(0,2,1))#matters for traffic datasets
        '''

        lr_weight = self.dct_norm(stack_dct)
        lr_weight = self.fc(stack_dct)
        lr_weight = self.dct_norm(lr_weight)

        # print("lr_weight",lr_weight.shape)
        return x * lr_weight  # result



class dct_channel_block_withConv(nn.Module):
    def __init__(self, channel):
        super(dct_channel_block_withConv, self).__init__()
        # self.avg_pool = nn.AdaptiveAvgPool1d(1) #innovation
        self.fc = nn.Sequential(
            nn.Linear(channel, channel * 2, bias=False),
            nn.Dropout(p=0.1),
            nn.ReLU(inplace=True),
            nn.Linear(channel * 2, channel, bias=False),
            nn.Sigmoid()
        )

        # self.dct_norm = nn.LayerNorm([512], eps=1e-6)

        self.dct_norm = nn.LayerNorm([channel], eps=1e-6)  # for lstm on length-wise
        # self.dct_norm = nn.LayerNorm([36], eps=1e-6)#for lstm on length-wise on ill with input =36

    def forward(self, x):
        b, c = x.size()  # (B,C,L) (32,96,512)

        # list = []
        # for i in range(c):
        #     freq = dct.dct(x[:, :, i])
        #     list.append(freq)
        #
        # stack_dct = torch.stack(list, dim=2)

        # change = x.transpose(2, 1)
        stack_dct = dct.dct(x,norm='ortho')

        # stack_dct = stack_dct.transpose(2, 1)

        # stack_dct = torch.tensor(stack_dct)
        '''
        for traffic mission:f_weight = self.dct_norm(f_weight.permute(0,2,1))#matters for traffic datasets
        '''

        lr_weight = self.dct_norm(stack_dct)
        lr_weight = self.fc(stack_dct)
        lr_weight = self.dct_norm(lr_weight)

        # print("lr_weight",lr_weight.shape)
        return x * lr_weight  # result


if __name__ == '__main__':
    # input_data = torch.Tensor([[1, 2, 3], [4, 5, 6]])  # [2, 3]
    x = torch.rand((32, 10, 64))
    print(x.shape)
    m = nn.Linear(64, 2)
    output = m(x)
    print(output.shape)  # [2, 2]