import torch
import torch.nn as nn
import numpy as np

from text_rnn import TextRNN
from config import BATCH_SIZE, N_EPOCHS, LOSS_AVG_MAX, HIDDEN_SIZE, EMBEDDING_SIZE, N_LAYERS
from generation import get_batch, evaluate


def create_parameters(idx_to_char):
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    model = TextRNN(input_size=len(idx_to_char), hidden_size=HIDDEN_SIZE, embedding_size=EMBEDDING_SIZE, n_layers=N_LAYERS, device=device)
    model.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, amsgrad=True)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
        optimizer,
        patience=5,
        verbose=True,
        factor=0.5
    )

    n_epochs = N_EPOCHS
    loss_avg = []

    return criterion, scheduler, n_epochs, loss_avg, device, model, optimizer


def check_loss(loss_avg, scheduler, model, char_to_idx, idx_to_char, device):
    if len(loss_avg) >= LOSS_AVG_MAX:
        mean_loss = np.mean(loss_avg)
        print(f'Loss: {mean_loss}')
        scheduler.step(mean_loss)
        loss_avg = []
        model.eval()
        predicted_text = evaluate(model, char_to_idx, idx_to_char, device=device)
        print(predicted_text)
    return loss_avg


def training(n_epochs, model, sequence, device, criterion, optimizer, loss_avg, scheduler, char_to_idx, idx_to_char):
    for epoch in range(n_epochs):
        model.train()
        train, target = get_batch(sequence)
        train = train.permute(1, 0, 2).to(device)
        target = target.permute(1, 0, 2).to(device)
        hidden = model.init_hidden(BATCH_SIZE)

        output, hidden = model(train, hidden)
        loss = criterion(output.permute(1, 2, 0), target.squeeze(-1).permute(1, 0))

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

        loss_avg.append(loss.item())
        loss_avg = check_loss(loss_avg, scheduler, model, char_to_idx, idx_to_char, device)