import torch
import torch.nn as nn
import numpy as np
import time
import math
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
torch.manual_seed(0)
np.random.seed(0)

input_window = 20
output_window = 1
batch_size = 64
device = torch.
device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):     
       super(PositionalEncoding, self).__init__()       
        pe = torch.zeros(max_len, d_model)      
          position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)       
           div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))     
              pe[:, 0::2] = torch.sin(position * div_term)       
               pe[:, 1::2] = torch.cos(position * div_term)       
                pe = pe.unsqueeze(0).transpose(0, 1)      
                  self.register_buffer('pe', pe)
    def forward(self, x):        
    return x + self.pe[:x.size(0), :]

class TransAm(nn.Module):   
 def __init__(self, feature_size=250, num_layers=1, dropout=0.1):       
  super(TransAm, self).__init__()        
  self.model_type = 'Transformer'        
  self.src_mask = None        
  self.pos_encoder = PositionalEncoding(feature_size)        
  self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=10, dropout=dropout)        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)        
  self.decoder = nn.Linear(feature_size, 1)        
  self.init_weights()
    def init_weights(self):        
    initrange = 0.1        
    self.decoder.bias.data.zero_()        
    self.decoder.weight.data.uniform_(-initrange, initrange)
    def forward(self, src):       
     if self.src_mask is None or self.src_mask.size(0) != len(src):         
        device = src.device           
         mask = self._generate_square_subsequent_mask(len(src)).to(device)           
          self.src_mask = mask
        src = self.pos_encoder(src)       
         output = self.transformer_encoder(src, self.src_mask)      
           output = self.decoder(output)      
             return output             
           def _generate_square_subsequent_mask(self, sz):       
            mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)        
            mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))       
             return mask

def create_inout_sequences(input_data, tw):    
inout_seq = []   
 L = len(input_data)   
  for i in range(L - tw):        
  train_seq = input_data[i:i + tw]      
    train_label = input_data[i + output_window:i + tw + output_window]       
     inout_seq.append((train_seq, train_label))    
     return torch.FloatTensor(inout_seq)

def get_data():
    series = pd.read_csv('./000001_Daily.csv', usecols=['Close'])    
    # series = pd.read_csv('./daily-min-temperatures.csv', usecols=['Temp'])    
    scaler = MinMaxScaler(feature_range=(-1, 1))  
      series = scaler.fit_transform(series.values.reshape(-1, 1)).reshape(-1)
    train_samples = int(0.7 * len(series))    train_data = series[:train_samples]    
    test_data = series[train_samples:]
    train_sequence = create_inout_sequences(train_data, input_window)   
     train_sequence = train_sequence[:-output_window]
    test_data = create_inout_sequences(test_data, input_window)   
     test_data = test_data[:-output_window]
    return train_sequence.to(device), test_data.to(device)

def get_batch(source, i, batch_size):   
 seq_len = min(batch_size, len(source) - 1 - i)  
   data = source[i:i + seq_len]   
    input = torch.stack(torch.stack([item[0] for item in data]).chunk(input_window, 1))      
   target = torch.stack(torch.stack([item[1] for item in data]).chunk(input_window, 1)) 
      return input, target

def train(train_data):  
  model.train()
    for batch_index, i in enumerate(range(0, len(train_data) - 1, batch_size)):       
     start_time = time.time()        
     total_loss = 0       
      data, targets = get_batch(train_data, i, batch_size)      
        optimizer.zero_grad()      
         output = model(data)       
          loss = criterion(output, targets)      
            loss.backward()      
              torch.nn.utils.clip_grad_norm_(model.parameters(), 0.7)      
                optimizer.step()
        total_loss += loss.item()      
          log_interval = int(len(train_data) / batch_size / 5)       
           if batch_index % log_interval == 0 and batch_index > 0:            
           cur_loss = total_loss / log_interval            
           elapsed = time.time() - start_time            
           print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.6f} | {:5.2f} ms | loss {:5.5f} | ppl {:8.2f}'                  
           .format(epoch, batch_index, len(train_data) // batch_size, scheduler.get_lr()[0], elapsed * 1000 / log_interval, cur_loss, math.exp(cur_loss)))

def evaluate(eval_model, data_source):   
 eval_model.eval()     
 total_loss = 0    
 eval_batch_size = 1000   
  with torch.no_grad():       
   for i in range(0, len(data_source) - 1, eval_batch_size):            
   data, targets = get_batch(data_source, i, eval_batch_size)            
   output = eval_model(data)            
   total_loss += len(data[0]) * criterion(output, targets).cpu().item()    
   return total_loss / len(data_source)

def plot_and_loss(eval_model, data_source, epoch):  
  eval_model.eval()    
  total_loss = 0.    
  test_result = torch.Tensor(0)    
  truth = torch.Tensor(0)  
    with torch.no_grad():       
   for i in range(0, len(data_source) - 1):           
    data, target = get_batch(data_source, i, 1)            
    output = eval_model(data)            
    total_loss += criterion(output, target).item()           
     test_result = torch.cat((test_result, output[-1].view(-1).cpu()), 0)           
      truth = torch.cat((truth, target[-1].view(-1).cpu()), 0)
      
    plt.plot(test_result, color="red")    plt.plot(truth, color="blue")    
    plt.grid(True, which='both')   
     plt.axhline(y=0, color='k')  
       plt.savefig('graph/transformer-epoch%d.png' % epoch)   
        plt.close()
    return total_loss / i

train_data, val_data = get_data()
model = TransAm().to(device)
criterion = nn.MSELoss()
lr = 0.005
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.95)
epochs = 200

for epoch in range(1, epochs + 1):    
epoch_start_time = time.time()    
train(train_data)

    if (epoch % 10 is 0):       
     val_loss = plot_and_loss(model, val_data, epoch)    
     else:   
          val_loss = evaluate(model, val_data)
    print('-' * 89)   
     print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.5f} | valid ppl {:8.2f}'.format(epoch, (                time.time() - epoch_start_time), val_loss, math.exp(val_loss)))    
     print('-' * 89)    scheduler.step()