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from argparse import ArgumentParser

import torch
from torch import nn
from torch.utils.data import DataLoader

from dataset import Dataset
from model import Model
from predict import predict


parser = ArgumentParser()
parser.add_argument('-d', '--device',  default='cpu',
                    help='device to train with')
parser.add_argument('-i', '--input',  default='data',
                    help='training data input file')
parser.add_argument('-e', '--epochs', default=100, type=int,
                    help='number of epochs to train for')
parser.add_argument('-s', '--seq-size', default=32, type=int,
                    help='sequence size')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    help='size of each training batch')
parser.add_argument('-m', '--embedding-dim', default=256, type=int,
                    help='size of the embedding')
parser.add_argument('-l', '--lstm-size', default=256, type=int,
                    help='size of the LSTM hidden state')
parser.add_argument('-a', '--layers', default=3, type=int,
                    help='number of LSTM layers')
parser.add_argument('-r', '--dropout', default=0.2, type=int,
                    help='how much dropout to apply')
parser.add_argument('-n', '--max-norm', default=5, type=int,
                    help='maximum norm for gradient clipping')
args = parser.parse_args()


# Prepare dataloader
dataset = Dataset(args.input, args.seq_size)
dataloader = DataLoader(dataset, args.batch_size)
print(len(dataloader))


# Prepare model
device = torch.device(args.device)
model = Model(dataset, args.embedding_dim, args.lstm_size,
              args.layers, args.dropout).to(device)
print(model)


loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)


for t in range(args.epochs):
    state_h, state_c = model.zero_state(args.batch_size)
    state_h = state_h.to(device)
    state_c = state_c.to(device)

    iteration = 0

    print(len(dataloader))
    for batch, (X, y) in enumerate(dataloader):
        iteration += 1

        model.train()

        optimizer.zero_grad()

        X = torch.tensor(X).to(device)
        y = torch.tensor(y).to(device)

        # Compute prediction error
        logits, (state_h, state_c) = model(X, (state_h, state_c))
        loss = loss_fn(logits.transpose(1, 2), y)

        loss_value = loss.item()

        # Backpropogation
        loss.backward()

        state_h = state_h.detach()
        state_c = state_c.detach()

        _ = torch.nn.utils.clip_grad_norm_(
            model.parameters(), args.max_norm)

        optimizer.step()

        if iteration % 1 == 0:
            print('Epoch: {}/{}'.format(t, args.epochs),
                  'Iteration: {}'.format(iteration),
                  'Loss: {}'.format(loss_value))

        if iteration % 1 == 0:
            print(' '.join(predict(args.device, dataset, model, 'i am')))
            #torch.save(model.state_dict(),
            #           'checkpoint/model-{}.pth'.format(iteration))