text: import os
  
  from sklearn.metrics import mean_squared_error
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import OneHotEncoder
  import numpy as np
  import argparse
  import contextlib
  import arithmeticcoding_fast
  import json
  from tqdm import tqdm
  import struct
  import tempfile
  import shutil
  import torch
  from torch import nn
  from torch.utils.data import TensorDataset, DataLoader
  import torchvision.datasets as dsets
  import torchvision.transforms as transforms
  import torch.nn.functional as F
  import numpy as np
  import torch.quantization
  import zipfile
  
  torch.manual_seed(1)
  
  def compress_model(args,device):
      class LSTM(nn.Module):
          def __init__(self, num_classes, hidden_size1=32, hidden_size2=32, num_layers=2):
              super(LSTM, self).__init__()
              self.hidden_size1 = hidden_size1
              self.hidden_size2 = hidden_size2
              self.num_layers = num_layers
              self.embedding= nn.Embedding(num_classes, 32).to(device)
              self.lstm = nn.LSTM(32, hidden_size1, num_layers, batch_first=True).to(device)
              self.fc1 = nn.Linear(hidden_size1, hidden_size2).to(device)
              self.fc2 = nn.Linear(hidden_size2, num_classes).to(device)
              
  
          def forward(self, x):
              # initialize
              h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size1).to(device)
              c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size1).to(device)
              
              out=self.embedding(x[:,:,0].long())
              out, (h_n, h_c) = self.lstm(out, (h0, c0))
              out =self.fc1(out[:, -1, :])
              out =self.fc2(nn.ReLU()(out))
  
              return out
      # load the data
      np.random.seed(0)
  
      series = np.load(args.sequence_npy_file)
      series = series.reshape(-1, 1)
  
      onehot_encoder = OneHotEncoder(sparse=False)
      onehot_encoded = onehot_encoder.fit(series)
  
      args.batch_size=int(len(series)/10000)
      batch_size = args.batch_size
      timesteps = 64
  
      with open(args.params_file, 'r') as f:
              params = json.load(f)
  
      params['len_series'] = len(series)
      params['bs'] = batch_size
      params['timesteps'] = timesteps
  
      with open(args.output_file_prefix+'.params','w') as f:
              json.dump(params, f, indent=4)
  
      alphabet_size = len(params['id2char_dict'])
  
      def strided_app(a, L, S):  # Window len = L, Stride len/stepsize = S
          nrows = ((a.size - L) // S) + 1
          n = a.strides[0]
          return np.lib.stride_tricks.as_strided(
              a, shape=(nrows, L), strides=(S * n, n), writeable=False)
  
      series = series.reshape(-1)
      data = strided_app(series, timesteps+1, 1)
  
      X = data[:, :-1]
      Y_original = data[:, -1:]
      Y = onehot_encoder.transform(Y_original)
  
      l = int(len(series)/batch_size)*batch_size
  
      # Hyper Parameters       
      num_epochs=args.num_epochs      
      hidden_size1 = args.hidden_size1
      hidden_size2 = args.hidden_size2
      num_layers = args.num_layers
      num_classes = alphabet_size
      lr = 0.001 
  
  
      if args.model_name=="LSTM":
          model = LSTM(num_classes,hidden_size1, hidden_size2, num_layers)
      elif args.model_name=="GRU":
          model = GRU(num_classes,hidden_size1, hidden_size2, num_layers)
      elif args.model_name=="biLSTM":
          model = biLSTM(num_classes,hidden_size1, hidden_size2, num_layers)
      elif args.model_name=="biGRU": 
          model = biGRU(num_classes,hidden_size1, hidden_size2, num_layers)
  
      # unzip the compressed models
      zip_path="../data/trained_models/"+args.data_name+"/"+args.file_path+args.append+ ".zip"
      save_path="../data/trained_models/"+args.data_name
      with zipfile.ZipFile(zip_path, 'r') as zip_ref:
          zip_ref.extractall(save_path)
  
      model=model.to('cpu')
      if args.quantization=="yes":
          # load the quantized model weights
          model = torch.quantization.quantize_dynamic(
              model, {nn.LSTM, nn.Linear}, dtype=torch.qint8
          )
          model.load_state_dict(torch.load(args.model_weights_file))
      else:
          #load the model weights
          model.load_state_dict(torch.load(args.model_weights_file))
      model=model.to(device)
          
      def predict_lstm(X, y, y_original, timesteps, bs, alphabet_size, model_name, final_step=False):       
              if not final_step:
                      num_iters = int((len(X)+timesteps)/bs)
                      ind = np.array(range(bs))*num_iters
                      
                      # open compressed files and compress first few characters using
                      # uniform distribution
                      f = [open(args.temp_file_prefix+'.'+str(i),'wb') for i in range(bs)]
                      bitout = [arithmeticcoding_fast.BitOutputStream(f[i]) for i in range(bs)]
                      enc = [arithmeticcoding_fast.ArithmeticEncoder(32, bitout[i]) for i in range(bs)]
                      prob = np.ones(alphabet_size)/alphabet_size
                      cumul = np.zeros(alphabet_size+1, dtype = np.uint64)
                      cumul[1:] = np.cumsum(prob*10000000 + 1)        
                      for i in range(bs):
                              for j in range(min(timesteps, num_iters)):
                                      enc[i].write(cumul, X[ind[i],j])
                      cumul = np.zeros((bs, alphabet_size+1), dtype = np.uint64)
                      for j in (range(num_iters - timesteps)):
                              x=torch.Tensor(X[ind,:])
                              x = x.reshape(-1,timesteps, args.input_size).to(device)
                              outputs = model(x)
                              prob=F.softmax(outputs).data.cpu().numpy()
                              cumul[:,1:] = np.cumsum(prob*10000000 + 1, axis = 1)
                              for i in range(bs):
                                      enc[i].write(cumul[i,:], y_original[ind[i]])
                              ind = ind + 1
                      # close files
                      for i in range(bs):
                              enc[i].finish()
                              bitout[i].close()
                              f[i].close()            
              else:
                      f = open(args.temp_file_prefix+'.last','wb')
                      bitout = arithmeticcoding_fast.BitOutputStream(f)
                      enc = arithmeticcoding_fast.ArithmeticEncoder(32, bitout)
                      prob = np.ones(alphabet_size)/alphabet_size
                      cumul = np.zeros(alphabet_size+1, dtype = np.uint64)
                      cumul[1:] = np.cumsum(prob*10000000 + 1)        
  
                      for j in range(timesteps):
                              enc.write(cumul, X[0,j])
                      for i in (range(len(X))):
                              x=torch.Tensor(X[i,:])
                              x = x.reshape(-1,timesteps, args.input_size).to(device)
                              outputs = model(x)
                              prob=F.softmax(outputs).data.cpu().numpy()
                              cumul[1:] = np.cumsum(prob*10000000 + 1)
                              enc.write(cumul, y_original[i][0])
                      enc.finish()
                      bitout.close()
                      f.close()
              return
  
  
      # variable length integer encoding http://www.codecodex.com/wiki/Variable-Length_Integers
      def var_int_encode(byte_str_len, f):
              while True:
                      this_byte = byte_str_len&127
                      byte_str_len >>= 7
                      if byte_str_len == 0:
                              f.write(struct.pack('B',this_byte))
                              break
                      f.write(struct.pack('B',this_byte|128))
                      byte_str_len -= 1
  
      # compress the data 
      predict_lstm(X, Y, Y_original, timesteps, batch_size, alphabet_size, args.model_name)
  
      if l < len(series)-timesteps:
              predict_lstm(X[l:,:], Y[l:,:], Y_original[l:], timesteps, 1, alphabet_size, args.model_name, final_step = True)
      else:
              f = open(args.temp_file_prefix+'.last','wb')
              bitout = arithmeticcoding_fast.BitOutputStream(f)
              enc = arithmeticcoding_fast.ArithmeticEncoder(32, bitout) 
              prob = np.ones(alphabet_size)/alphabet_size
              
              cumul = np.zeros(alphabet_size+1, dtype = np.uint64)
              cumul[1:] = np.cumsum(prob*10000000 + 1)        
              for j in range(l, len(series)):
                      enc.write(cumul, series[j])
              enc.finish()
              bitout.close() 
              f.close()
  
      # combine files into one file
      f = open(args.output_file_prefix+'.combined','wb')
      for i in range(batch_size):
              f_in = open(args.temp_file_prefix+'.'+str(i),'rb')
              byte_str = f_in.read()
              byte_str_len = len(byte_str)
              var_int_encode(byte_str_len, f)
              f.write(byte_str)
              f_in.close()
      f_in = open(args.temp_file_prefix+'.last','rb')
      byte_str = f_in.read()
      byte_str_len = len(byte_str)
      var_int_encode(byte_str_len, f)
      f.write(byte_str)
      f_in.close()
      f.close()
      shutil.rmtree(args.temp_dir)
  
  def compression(file_path,quantization,Setting):
      device = torch.device('cuda' if (torch.cuda.is_available() and quantization=="no") else 'cpu')      
      args=Setting(file_path,quantization)
      compress_model(args,device)
  
      compressed_path="../data/compressed/"+args.data_name+"/"+file_path+args.append+".compressed.combined"
      size=os.path.getsize(compressed_path)/1024
      print("Size of compressed data in Deepzip with "+file_path+args.append+": %.2f KB" % size)
  
      return("Size of compressed data in Deepzip with "+file_path+args.append+": %.2f KB" % size)
  
  def check_size(file_path,Setting):
      args=Setting(file_path,0)
      compressed_path="../data/compressed/"+args.data_name+"/"+file_path+args.append+".compressed.combined"
      size=os.path.getsize(compressed_path)/1024
      print("Size of compressed data in Deepzip with "+file_path+args.append+": %.2f KB" % size)