import sys

import os

import collections

import numpy as np

lib_path = 'I:/code'

if not os.path.exists(lib_path):

  lib_path = '/media/6T/.tianle/.lib'

if os.path.exists(lib_path) and lib_path not in sys.path:

  sys.path.append(lib_path)

import torch

import torch.nn as nn

from dl.models.basic_models import DenseLinear, get_list, get_attr

from dl.utils.train import cosine_similarity

from autoencoder.autoencoder import *

# Had run in jupyter notebook but not here

# test Loss_view_similarity

x = torch.randn(11, 10)

model = MultiviewAE(in_dims=[2,3,5], hidden_dims=[7], out_dim=11)

loss_fn_g = Loss_view_similarity(sections=7, loss_type='hub', explicit_target=True, 

    cal_target='mean-feature', target=None, fusion_type='multiply', graph_laplacian=False)

loss_fn_d = Loss_view_similarity(sections=7, loss_type='hub', explicit_target=False, 

    cal_target='mean-feature', target=None, fusion_type='sum', graph_laplacian=True)

loss_fn_c = Loss_view_similarity(sections=7, loss_type='hub', explicit_target=True, 

    cal_target='mean-similarity', target=None, fusion_type='sum', graph_laplacian=False)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)

loss_history = []

for i in range(100):

  xs = model(x)[-1]

  loss_g = loss_fn_g(xs)

  optimizer.zero_grad()

  loss_g.backward()

  optimizer.step()

  with torch.no_grad():

    loss_d = loss_fn_d(xs)

    loss_c = loss_fn_c(xs)

  loss_history.append([loss_g.item(), loss_d.item(), loss_c.item()])

plt.plot(loss_history)

plt.show()

xs = xs.split(7, dim=1)

circle_similarity_mats = [cosine_similarity(xs[i-1], xs[i]) for i in range(1, len(xs))]

self_similarity_mats = [cosine_similarity(xs[i]) for i in range(len(xs))]

print([(x.mean().item(), x.std().item()) for x in xs])

print([(m.mean().item(), m.std().item())  for m in circle_similarity_mats])

print([(m.mean().item(), m.std().item())  for m in self_similarity_mats])