import argparse
import time
import math

import torch
import torch.nn as nn
import torch.optim as optim

import data

parser = argparse.ArgumentParser(description='PyTorch Language Modeling')
parser.add_argument('--data', type=str, default='penn',
                    help='data corpus (penn, wikitext-2, wikitext-103)')
parser.add_argument('--model', type=str, default='DNC',
                    help='type of recurrent net (LSTM, Transformer, DNC)')
parser.add_argument('--emsize', type=int, default=50,
                    help='size of word embeddings')
parser.add_argument('--nhid', type=int, default=200,
                    help='number of hidden units per layer')
parser.add_argument('--nlayers', type=int, default=2,
                    help='number of layers')
parser.add_argument('--lr', type=float, default=20,
                    help='initial learning rate')
parser.add_argument('--clip', type=float, default=0.25,
                    help='gradient clipping')
parser.add_argument('--epochs', type=int, default=40,
                    help='upper epoch limit')
parser.add_argument('--batch_size', type=int, default=20, metavar='N',
                    help='batch size')
parser.add_argument('--bptt', type=int, default=35,
                    help='sequence length')
parser.add_argument('--dropout', type=float, default=0.2,
                    help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--tied', action='store_true',
                    help='tie the word embedding and softmax weights')
parser.add_argument('--seed', type=int, default=1111,
                    help='random seed')
parser.add_argument('--cuda', action='store_true',
                    help='use CUDA')
parser.add_argument('--log-interval', type=int, default=200, metavar='N',
                    help='report interval')

parser.add_argument('--nhead', type=int, default=2,
                    help='the number of heads in the encoder/decoder of the transformer model')

args = parser.parse_args()

# Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)

device = torch.device("cuda" if args.cuda else "cpu")

name = args.data + '_' + args.model
path = './res/' + name + '.pt'

###############################################################################
# Load data
###############################################################################

corpus = data.Corpus('./data/' + args.data)

# Starting from sequential data, batchify arranges the dataset into columns.
# For instance, with the alphabet as the sequence and batch size 4, we'd get
# ┌ a g m s ┐
# │ b h n t │
# │ c i o u │
# │ d j p v │
# │ e k q w │
# └ f l r x ┘.
# These columns are treated as independent by the model, which means that the
# dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient
# batch processing.


def batchify(data, bsz):
    # Work out how cleanly we can divide the dataset into bsz parts.
    nbatch = data.size(0) // bsz
    # Trim off any extra elements that wouldn't cleanly fit (remainders).
    data = data.narrow(0, 0, nbatch * bsz)
    # Evenly divide the data across the bsz batches.
    data = data.view(bsz, -1).t().contiguous()
    return data.to(device)


eval_batch_size = 10
train_data = batchify(corpus.train, args.batch_size)
val_data = batchify(corpus.valid, eval_batch_size)
test_data = batchify(corpus.test, eval_batch_size)

###############################################################################
# Build the model
###############################################################################

ntokens = len(corpus.dictionary)
if args.model == 'LSTM':
    from model.rnn_lm import RNNLM
    model = RNNLM(args.model, ntokens, args.emsize, args.nhid, args.nlayers, args.dropout, args.tied).to(device)
elif args.model == 'Transformer':
    from model.transformer_lm import TransformerLM
    model = TransformerLM(ntokens, args.emsize, args.nhead, args.nhid, args.nlayers, args.dropout).to(device)
elif args.model == 'DNC':
    from model.dnc_lm import DNCLM
    model = DNCLM(ntoken=ntokens, ninp=args.emsize, nhid=args.nhid, nlayers=args.nlayers, dropout=args.dropout).to(device)

criterion = nn.CrossEntropyLoss()
print(args)


###############################################################################
# Training code
###############################################################################

def detach_hidden(h):
    """Wraps hidden states in new Tensors, to detach them from their history."""
    if isinstance(h, torch.Tensor):
        return h.detach()
    elif h is None:
        return h
    elif isinstance(h, list):
        return list(detach_hidden(v) for v in h)
    elif isinstance(h, tuple):
        return tuple(detach_hidden(v) for v in h)
    elif isinstance(h, dict):
        for k, v in h.items():
            h[k] = detach_hidden(v)
        return h
    else:
        return h


# get_batch subdivides the source data into chunks of length args.bptt.
# If source is equal to the example output of the batchify function, with
# a bptt-limit of 2, we'd get the following two Variables for i = 0:
# ┌ a g m s ┐ ┌ b h n t ┐
# └ b h n t ┘ └ c i o u ┘
# Note that despite the name of the function, the subdivison of data is not
# done along the batch dimension (i.e. dimension 1), since that was handled
# by the batchify function. The chunks are along dimension 0, corresponding
# to the seq_len dimension in the LSTM.

def get_batch(source, i):
    seq_len = min(args.bptt, len(source) - 1 - i)
    data = source[i:i+seq_len]
    target = source[i+1:i+1+seq_len].view(-1)
    return data, target


def evaluate(data_source):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    total_loss = 0.
    ntokens = len(corpus.dictionary)
    if args.model != 'Transformer':
        hidden = model.init_hidden(eval_batch_size)
    with torch.no_grad():
        for i in range(0, data_source.size(0) - 1, args.bptt):
            data, targets = get_batch(data_source, i)
            if args.model == 'Transformer':
                output = model(data)
            else:
                output, hidden = model(data, hidden)
                hidden = detach_hidden(hidden)
            output_flat = output.view(-1, ntokens)
            total_loss += len(data) * criterion(output_flat, targets).item()
    return total_loss / (len(data_source) - 1)


def train():
    # Turn on training mode which enables dropout.
    model.train()
    total_loss = 0.
    start_time = time.time()
    ntokens = len(corpus.dictionary)
    if args.model != 'Transformer':
        hidden = model.init_hidden(args.batch_size)
    for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
        data, targets = get_batch(train_data, i)
        # Starting each batch, we detach the hidden state from how it was previously produced.
        # If we didn't, the model would try backpropagating all the way to start of the dataset.
        model.zero_grad()
        if args.model == 'LSTM':
            hidden = detach_hidden(hidden)
            output, hidden = model(data, hidden)
        elif args.model == 'Transformer':
            output = model(data)
        elif args.model == 'DNC':
            hidden = detach_hidden(hidden)
            output, hidden = model(data, hidden)
        loss = criterion(output.view(-1, ntokens), targets)
        loss.backward()

        # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
        torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
        optimizer.step()

        total_loss += loss.item()

        if batch % args.log_interval == 0 and batch > 0:
            cur_loss = total_loss / args.log_interval
            elapsed = time.time() - start_time
            print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2e} | ms/batch {:5.2f} | '
                  'loss {:5.2f} | ppl {:8.2f}'.format(
                epoch, batch, len(train_data) // args.bptt, lr,
                elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
            total_loss = 0
            start_time = time.time()


# Loop over epochs.
lr = args.lr
best_val_loss = None

# optimizer = optim.Adam(model.parameters(), lr=lr)
optimizer = optim.SGD(model.parameters(), lr=lr)

# At any point you can hit Ctrl + C to break out of training early.
try:
    for epoch in range(args.epochs):
        epoch_start_time = time.time()
        train()
        val_loss = evaluate(val_data)
        print('-' * 89)
        print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
              'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
                                         val_loss, math.exp(val_loss)))
        print('-' * 89)
        # Save the model if the validation loss is the best we've seen so far.
        if not best_val_loss or val_loss < best_val_loss:
            torch.save(model, path)
            best_val_loss = val_loss
        else:
            # Anneal the learning rate if no improvement has been seen in the validation dataset.
            lr /= 4
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr
except KeyboardInterrupt:
    print('-' * 89)
    print('Exiting from training early')

# Load the best saved model.
model = torch.load(path)
# after load the rnn params are not a continuous chunk of memory
# this makes them a continuous chunk, and will speed up forward pass
# Currently, only rnn model supports flatten_parameters function.
if args.model in ['LSTM']:
    model.rnn.flatten_parameters()

# Run on test data.
test_loss = evaluate(test_data)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
    test_loss, math.exp(test_loss)))
print('=' * 89)
