import pandas as pd
import numpy as np
import torch
import gensim.downloader as gensim
from nltk.tokenize import word_tokenize

x_train = pd.read_table('train/in.tsv', sep='\t', header = None, error_bad_lines = False, quoting = 3)
y_train = pd.read_table('train/expected.tsv', sep='\t', header = None, quoting = 3)
y_train = y_train[0]
x_dev = pd.read_table('dev-0/in.tsv', sep='\t', header = None, quoting = 3)
x_test = pd.read_table('test-A/in.tsv', sep='\t', header = None, quoting = 3)

x_train = x_train[0].str.lower()
x_dev = x_dev[0].str.lower()
x_test = x_test[0].str.lower()

x_train = [word_tokenize(x) for x in x_train]
x_dev = [word_tokenize(x) for x in x_dev]
x_test = [word_tokenize(x) for x in x_test]

word2vec = gensim.load('glove-wiki-gigaword-50')

def document_vector(doc):
    return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], axis=0)

x_train = [document_vector(doc) for doc in x_train]
x_dev = [document_vector(doc) for doc in x_dev]
x_test = [document_vector(doc) for doc in x_test]

class NeuralNetworkModel(torch.nn.Module):
    def __init__(self, features):
        super(NeuralNetworkModel, self).__init__()
        self.fc1 = torch.nn.Linear(50, features)
        self.fc2 = torch.nn.Linear(features, 1)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x
    
nn_model = NeuralNetworkModel(100)
BATCH_SIZE = 5
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)

for epoch in range(5):
    nn_model.train()
    for i in range(0, y_train.shape[0], BATCH_SIZE):
        X = x_train[i:i+BATCH_SIZE]
        X = torch.tensor(X)
        y = y_train[i:i+BATCH_SIZE]
        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)

        outputs = nn_model(X.float())
        loss = criterion(outputs, y)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

y_dev = []
y_test = []

nn_model.eval()

with torch.no_grad():
    for i in range(0, len(x_dev), BATCH_SIZE):
        X = x_dev[i:i+BATCH_SIZE]
        X = torch.tensor(X)
        
        outputs = nn_model(X.float())
        
        y = (outputs > 0.5)
        y_dev.extend(y)

    for i in range(0, len(x_test), BATCH_SIZE):
        X = x_test[i:i+BATCH_SIZE]
        X = torch.tensor(X)

        outputs = nn_model(X.float())

        y = (outputs > 0.5)
        y_test.extend(y)

y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)

Y_dev = pd.DataFrame({'label':y_dev})
Y_test = pd.DataFrame({'label':y_test})

Y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False,  header=False)
Y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False,  header=False)