import torch
import torch.nn as nn

import numpy as np

import models

import pickle

import time
start_time = time.time()

PAD = "<pad>"
BOS = "<bos>"
EOS = "<eos>"

word2idx = pickle.load(open("word2idx.pkl", "rb"))
wordvecs = pickle.load(open("wordvecs.pkl", "rb"))
slots = pickle.load(open("slots.pkl", "rb"))
intents = pickle.load(open("intents.pkl", "rb"))
num_words = len(word2idx)
num_intent = 7
num_slot = 72
filter_count = 300
dropout = 0
embedding_dim = 100


def pad_query(sequence):
        sequence = [word2idx[BOS]] + sequence + [word2idx[EOS]]
        sequence = sequence[:50]
        sequence = np.pad(sequence, (0, 50 - len(sequence)), mode='constant', constant_values=(word2idx[PAD],))
        return sequence


query = "What's the weather like in Great Mills right now?"
q = query.lower().replace("'", " ").replace("?", " ").strip()
true_length = [len(q.split()), 0, 0, 0, 0, 0, 0 ,0]
qq = torch.from_numpy(pad_query([word2idx[word] for word in q.split()]))
    
model = models.CNNJoint(num_words, embedding_dim, num_intent, num_slot, (filter_count,), 5, dropout, wordvecs)
model.eval()

model.load_state_dict(torch.load('snips_joint', map_location=torch.device('cpu')))
criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)

pad_tensor = torch.from_numpy(pad_query([word2idx[w] for w in []]))
batch = torch.stack([qq, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor])    

pred_intent, pred_slots = model(batch)

slt = [str(item) for batch_num, sublist in enumerate(pred_slots.max(1)[1].tolist()) for item in sublist[1:true_length[batch_num] + 1]]                                                                                                                      
out_slots = [slots[int(c)] for c in slt]

itnt = pred_intent.max(1)[1].tolist()[0]
out_intent = intents[itnt]

print("Input: {}\nIntent: {}\nSlots: {}".format(query, out_intent, out_slots))

print("--- %s seconds ---" % (time.time() - start_time))