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


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

x_train = x_train.content.str.lower()
x_dev = x_dev.content.str.lower()
x_test = x_test.content.str.lower()

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

word2vec = gensim.downloader.load("word2vec-google-news-300")

def document_vector(doc):
    """Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
    return np.mean([word2vec[w] for w in doc if w in word2vec] or [np.zeros(300)], 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 NeuralNetwork(torch.nn.Module): 
    def __init__(self, hidden_size):
        super(NeuralNetwork, self).__init__()
        self.l1 = torch.nn.Linear(300, hidden_size)
        self.l2 = torch.nn.Linear(hidden_size, 1)

    def forward(self, x):
        x = self.l1(x)
        x = torch.relu(x)
        x = self.l2(x)
        x = torch.sigmoid(x)
        return x


hidden_size = 600
epochs = 5
batch_size = 15
model = NeuralNetwork(hidden_size)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(epochs):
    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 = model(X.float())
        loss = criterion(outputs, y)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

y_dev = []
y_test = []

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 = model(X.float())       
        prediction = (outputs > 0.5)
        y_dev.extend(prediction)

    for i in range(0, len(x_test), batch_size):
        X = x_test[i:i+batch_size]
        X = torch.tensor(X)
        outputs = model(X.float())
        y = (outputs > 0.5)
        y_test.extend(prediction)


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)