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start = np.random.randint(0,len(valid_list) - 150)
seed_list = valid_list[start:start+20]

current_string = [string2int[string] for string in seed_list]
for i in range(20):
  input = torch.tensor(current_string,dtype=torch.long).cuda().reshape(1,-1)
  output = model(input)
  _, predicted = torch.max(output[:,-1,:], 1)
  current_string.append(predicted.item())
print(' '.join(seed_list))

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class ModelGRU(nn.Module):
    def __init__(self):
        super().__init__()
        self.i_h = nn.Embedding(nv,nh)
        self.rnn = nn.GRU(nh, nh, 2, batch_first=True)
        self.h_o = nn.Linear(nh,nv)
        self.bn1 = nn.BatchNorm1d(num_features=nh)
        
    def forward(self, x):
      self.h = torch.zeros(2, x.shape[0], nh).cuda()

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class ModelRNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.i_h = nn.Embedding(nv,nh)
        self.rnn = nn.RNN(nh,nh, batch_first=True)
        self.h_o = nn.Linear(nh,nv)
        self.bn = nn.BatchNorm1d(num_features=nh)
       
        
    def forward(self, x):

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#Build the model and add to graphics card
model = ModelRNNBasic().cuda()

# Define Loss, Optimizer
lr=1e-3
opt = torch.optim.Adam(model.parameters(), lr=lr)
def loss_func(input,target):
  return F.cross_entropy(input.permute(0,2,1), target)

losses = []

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nv = len(vocab)
nh = 64

class ModelRNNBasic(nn.Module):
    def __init__(self):
        super().__init__()
        self.i_h = nn.Embedding(nv,nh)
        self.h_h = nn.Linear(nh,nh)
        self.h_o = nn.Linear(nh,nv)
        self.bn = nn.BatchNorm1d(nh)

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class DataLM():
  
  def __init__(self, train_data, index=0,bptt=10, bs=64):
      self.train_data = train_data
      self.index = index
      self.bptt = bptt
      self.bs = bs
      
  def __iter__(self):
    return self

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train_path = os.path.join('./train.txt')
valid_path = os.path.join('./valid.txt')

train_txt = ' , '.join(o.strip() for o in open(train_path).readlines())
valid_txt = ' , '.join(o.strip() for o in open(valid_path).readlines())

train_list = train_txt.split(' ')
valid_list = valid_txt.split(' ')

train_set = set(train_txt.split(' '))

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import requests
import pandas as pd
from bs4 import BeautifulSoup
import time
import json
import datetime
from pymongo import MongoClient
import pymongo

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FROM python:3.6
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
ENTRYPOINT [ "python", "-u", "main.py" ]

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#cloud-config
runcmd:
  - ufw allow 22/tcp
  - ufw allow 2376/tcp
  - ufw allow 2377/tcp
  - ufw allow 7946/tcp
  - ufw allow 7946/udp
  - ufw allow 4789/udp
  - ufw reload
  - systemctl restart docker