import os
import pandas as pd
import tensorflow as tf
import numpy as np
import torch
import torch.nn as nn
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization

os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

print('debug 1')
train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
dev_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')

train_text = train_df[0].tolist()
test_text = test_df[0].tolist()
dev_text = test_df[0].tolist()

text_data = train_text + test_text + dev_text

vectorize_layer = TextVectorization(max_tokens=5, output_mode="int")
text_data = tf.data.Dataset.from_tensor_slices(text_data)
vectorize_layer.adapt(text_data.batch(64))

inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="text")
outputs = vectorize_layer(inputs)
model = tf.keras.Model(inputs, outputs)

print('uwaga debug')
x_train = list(map(model.predict, train_text))
y_train = train_expected[0]
x_test = list(map(model.predict, test_text))
loss_function = nn.CrossEntropyLoss()

x_train = pd.DataFrame(x_train)
x_test = pd.DataFrame(x_test)
y_train = pd.DataFrame(y_train[0])

# (model.predict(["Murder in the forset!"]))


class FeedforwardNeuralNetModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(FeedforwardNeuralNetModel, self).__init__()
        # Linear function
        self.fc1 = nn.Linear(input_dim, hidden_dim)

        # Non-linearity
        self.sigmoid = nn.Sigmoid()

        # Linear function (readout)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # Linear function  # LINEAR
        out = self.fc1(x)

        # Non-linearity  # NON-LINEAR
        out = self.sigmoid(out)

        # Linear function (readout)  # LINEAR
        out = self.fc2(out)
        return out

num_epochs = 2

for epoch in range(num_epochs):
    if (epoch + 1) % 25 == 0:
        print("Epoch completed: " + str(epoch + 1))
    print(f"Epoch number: {epoch}")
    train_loss = 0
    for index, row in x_train.iterrows():
        print(index)

        # Forward pass to get output
        probs = x_train[0][index]
        # Get the target label
        target = y_train[0][index]

        # Calculate Loss: softmax --> cross entropy loss
        loss = loss_function(probs, target)
        # Accumulating the loss over time
        train_loss += loss.item()

        # Getting gradients w.r.t. parameters
        loss.backward()



    train_loss = 0


bow_ff_nn_predictions = []
original_lables_ff_bow = []
with torch.no_grad():
    for index, row in x_test.iterrows():
        probs = x_test[0][index]
        bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])

print(bow_ff_nn_predictions)