import os
from util import Model
import spacy
import torch
import numpy as np
import tqdm

def clean_string(str):
    str = str.replace('\n', '')
    return str

def extract_word(line):
    return line.split(" ")[1]

def line2word(line):
    word = extract_word(line)
    word = clean_string(word)
    return word

def find_interpunction(line, classes):
    result = [x for x in classes if x in line]
    if len(result) > 0:
        return result[0]
    else:
        return ['']

def words_to_vecs(list_of_words):
    return [nlp(x).vector for x in list_of_words]

def softXEnt(input, target):
    m = torch.nn.LogSoftmax(dim = 1)
    logprobs = m(input)
    return  -(target * logprobs).sum() / input.shape[0]

def compute_class_vector(mark, classes):
    result = np.zeros(len(classes))
    for x in range(len(classes)):
        if classes[x] == mark[0]:
            result[x] == 1
    return torch.tensor(result, dtype=torch.long)



data_dir = "./fa/poleval_final_dataset/train"
data_nopunc_dir = "./fa/poleval_final_dataset1/train"

data_paths = os.listdir(data_dir)
data_paths = [data_dir + "/" + x for x in data_paths]

classes = [',', '.', '?', '!', '-', ':', '...']
nlp = spacy.load("pl_core_news_sm")
context_size = 5

model = Model()
epochs = 5
output_prefix = "model"
hidden_state = torch.randn((2, 1, 300), requires_grad=True)
cell_state = torch.randn((2, 1, 300), requires_grad=True)

model.train()
optimizer = torch.optim.AdamW(model.parameters(), lr=0.02)
loss_function = softXEnt

for epoch in range(epochs):
    for path in tqdm.tqdm(data_paths):
        with open(path, "r") as file:
            list = file.readlines()[:-1]
            for i in range(0, len(list) - context_size - 1):
                model.zero_grad()
                x = list[i: i + context_size]
                x = [line2word(y) for y in x]
                x_1 = [line2word(list[i + context_size + 1])]
                x = x + x_1
                x = words_to_vecs(x)
                mark = find_interpunction(x, classes)
                mark = words_to_vecs(mark)
                
                x = torch.tensor(x, dtype=torch.float)
                mark = torch.tensor(mark, dtype=torch.float)

                output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
                output = output.squeeze(1)
                loss = loss_function(output, compute_class_vector(mark, classes))
                loss.backward()
                optimizer.step()
                hidden_state = hidden_state.detach()
                cell_state = cell_state.detach()

                """
                vector -> (96,), np nadarray
                """
    print("Epoch: {}".format(epoch))
    torch.save(
            model.state_dict(),
            os.path.join("./", f"{output_prefix}-{epoch}.pt"),
        )