import os
import time
from pathlib import Path
from collections import namedtuple

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

from torchtext import data
from torchtext import datasets

from model import RNNClassifier

ConfigGen = namedtuple('ConfigGen', 'vocab_dim out_dim cells birnn dropout fc1_dim fc2_dim type n_layers embed_dim hidden_size')
ConfigGen.__new__.__defaults__ = (None,) * len(ConfigGen._fields)
USERHOME = str(Path.home())
batch_size = 64
inputs = data.Field(lower=True)
answers = data.Field(sequential=False)

train, dev, test = datasets.SNLI.splits(inputs, answers)

inputs.build_vocab(train, dev, test)
vector = os.path.join(USERHOME, '.vector_cache', 'glove.6B.300d.txt.pt')
if os.path.isfile(vector):
    # TODO - make it customizable
    inputs.vocab.vectors = torch.load(vector)
else:
    inputs.vocab.load_vectors('glove.6B.300d')
answers.build_vocab(train)

train_iter, dev_iter, test_iter = data.BucketIterator.splits(
    (train, dev, test), batch_size=batch_size)


vocab_dim = len(inputs.vocab)
out_dim = len(answers.vocab)
print(f'inputs.vocab = {inputs.vocab}')
print(f'answers.vocab = {answers.vocab}')

embed_dim = 300
cells = 2
birnn = True
lr = 0.01
epochs = 10
if birnn:
    cells *= 2
dropout = 0.5
fc1_dim = 50
fc2_dim = 3
n_layers = 2
network_type = 'LSTM'
hidden_size = 1000
config = ConfigGen(
    vocab_dim, out_dim, cells, birnn, dropout, fc1_dim, fc2_dim, network_type, n_layers, embed_dim, hidden_size)
model = RNNClassifier(config)
model.embed.weight.data = inputs.vocab.vectors
# TODO - convert to cuda if required

criterion = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=lr)

iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False

for epoch in range(epochs):
    train_iter.init_epoch()
    n_correct, n_total = 0, 0
    for batch_idx, batch in enumerate(train_iter):
        model.train()
        opt.zero_grad()
        iterations += 1
        answer = model(batch)
        n_correct += (torch.max(answer, 1)
                      [1].view(batch.label.size()) == batch.label).sum()
        n_total += batch.batch_size
        train_acc = 100. * n_correct / n_total
        # labels starts from 1 but we need it to start from 0
        loss = criterion(answer, batch.label - 1)
        loss.backward()
        opt.step()
        if iterations % 5 == 0:
            model.eval()
            dev_iter.init_epoch()
            n_dev_correct, dev_loss = 0, 0
            for dev_batch_idx, dev_batch in enumerate(dev_iter):
                answer = model(dev_batch)
                n_dev_correct += (torch.max(answer, 1)
                                  [1].view(dev_batch.label.size()) == dev_batch.label).sum()
                dev_loss = criterion(answer, dev_batch.label - 1)
                print(dev_loss.item())
            dev_acc = 100. * n_dev_correct / len(dev)
fab8b46fa6fff03e4fb0f2d204be9379785b0044
<mask token>

<mask token>
inputs.build_vocab(train, dev, test)
<mask token>
if os.path.isfile(vector):
    inputs.vocab.vectors = torch.load(vector)
else:
    inputs.vocab.load_vectors('glove.6B.300d')
answers.build_vocab(train)
<mask token>
print(f'inputs.vocab = {inputs.vocab}')
print(f'answers.vocab = {answers.vocab}')
<mask token>
if birnn:
    cells *= 2
<mask token>
for epoch in range(epochs):
    train_iter.init_epoch()
    n_correct, n_total = 0, 0
    for batch_idx, batch in enumerate(train_iter):
        model.train()
        opt.zero_grad()
        iterations += 1
        answer = model(batch)
        n_correct += (torch.max(answer, 1)[1].view(batch.label.size()) ==
            batch.label).sum()
        n_total += batch.batch_size
        train_acc = 100.0 * n_correct / n_total
        loss = criterion(answer, batch.label - 1)
        loss.backward()
        opt.step()
        if iterations % 5 == 0:
            model.eval()
            dev_iter.init_epoch()
            n_dev_correct, dev_loss = 0, 0
            for dev_batch_idx, dev_batch in enumerate(dev_iter):
                answer = model(dev_batch)
                n_dev_correct += (torch.max(answer, 1)[1].view(dev_batch.
                    label.size()) == dev_batch.label).sum()
                dev_loss = criterion(answer, dev_batch.label - 1)
                print(dev_loss.item())
            dev_acc = 100.0 * n_dev_correct / len(dev)

<mask token>
ConfigGen = namedtuple('ConfigGen',
    'vocab_dim out_dim cells birnn dropout fc1_dim fc2_dim type n_layers embed_dim hidden_size'
    )
ConfigGen.__new__.__defaults__ = (None,) * len(ConfigGen._fields)
USERHOME = str(Path.home())
batch_size = 64
inputs = data.Field(lower=True)
answers = data.Field(sequential=False)
train, dev, test = datasets.SNLI.splits(inputs, answers)
inputs.build_vocab(train, dev, test)
vector = os.path.join(USERHOME, '.vector_cache', 'glove.6B.300d.txt.pt')
if os.path.isfile(vector):
    inputs.vocab.vectors = torch.load(vector)
else:
    inputs.vocab.load_vectors('glove.6B.300d')
answers.build_vocab(train)
train_iter, dev_iter, test_iter = data.BucketIterator.splits((train, dev,
    test), batch_size=batch_size)
vocab_dim = len(inputs.vocab)
out_dim = len(answers.vocab)
print(f'inputs.vocab = {inputs.vocab}')
print(f'answers.vocab = {answers.vocab}')
embed_dim = 300
cells = 2
birnn = True
lr = 0.01
epochs = 10
if birnn:
    cells *= 2
dropout = 0.5
fc1_dim = 50
fc2_dim = 3
n_layers = 2
network_type = 'LSTM'
hidden_size = 1000
config = ConfigGen(vocab_dim, out_dim, cells, birnn, dropout, fc1_dim,
    fc2_dim, network_type, n_layers, embed_dim, hidden_size)
model = RNNClassifier(config)
model.embed.weight.data = inputs.vocab.vectors
criterion = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=lr)
iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False
for epoch in range(epochs):
    train_iter.init_epoch()
    n_correct, n_total = 0, 0
    for batch_idx, batch in enumerate(train_iter):
        model.train()
        opt.zero_grad()
        iterations += 1
        answer = model(batch)
        n_correct += (torch.max(answer, 1)[1].view(batch.label.size()) ==
            batch.label).sum()
        n_total += batch.batch_size
        train_acc = 100.0 * n_correct / n_total
        loss = criterion(answer, batch.label - 1)
        loss.backward()
        opt.step()
        if iterations % 5 == 0:
            model.eval()
            dev_iter.init_epoch()
            n_dev_correct, dev_loss = 0, 0
            for dev_batch_idx, dev_batch in enumerate(dev_iter):
                answer = model(dev_batch)
                n_dev_correct += (torch.max(answer, 1)[1].view(dev_batch.
                    label.size()) == dev_batch.label).sum()
                dev_loss = criterion(answer, dev_batch.label - 1)
                print(dev_loss.item())
            dev_acc = 100.0 * n_dev_correct / len(dev)

import os
import time
from pathlib import Path
from collections import namedtuple
import torch
from torch import optim
import torch.nn as nn
from torchtext import data
from torchtext import datasets
from model import RNNClassifier
ConfigGen = namedtuple('ConfigGen',
    'vocab_dim out_dim cells birnn dropout fc1_dim fc2_dim type n_layers embed_dim hidden_size'
    )
ConfigGen.__new__.__defaults__ = (None,) * len(ConfigGen._fields)
USERHOME = str(Path.home())
batch_size = 64
inputs = data.Field(lower=True)
answers = data.Field(sequential=False)
train, dev, test = datasets.SNLI.splits(inputs, answers)
inputs.build_vocab(train, dev, test)
vector = os.path.join(USERHOME, '.vector_cache', 'glove.6B.300d.txt.pt')
if os.path.isfile(vector):
    inputs.vocab.vectors = torch.load(vector)
else:
    inputs.vocab.load_vectors('glove.6B.300d')
answers.build_vocab(train)
train_iter, dev_iter, test_iter = data.BucketIterator.splits((train, dev,
    test), batch_size=batch_size)
vocab_dim = len(inputs.vocab)
out_dim = len(answers.vocab)
print(f'inputs.vocab = {inputs.vocab}')
print(f'answers.vocab = {answers.vocab}')
embed_dim = 300
cells = 2
birnn = True
lr = 0.01
epochs = 10
if birnn:
    cells *= 2
dropout = 0.5
fc1_dim = 50
fc2_dim = 3
n_layers = 2
network_type = 'LSTM'
hidden_size = 1000
config = ConfigGen(vocab_dim, out_dim, cells, birnn, dropout, fc1_dim,
    fc2_dim, network_type, n_layers, embed_dim, hidden_size)
model = RNNClassifier(config)
model.embed.weight.data = inputs.vocab.vectors
criterion = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=lr)
iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False
for epoch in range(epochs):
    train_iter.init_epoch()
    n_correct, n_total = 0, 0
    for batch_idx, batch in enumerate(train_iter):
        model.train()
        opt.zero_grad()
        iterations += 1
        answer = model(batch)
        n_correct += (torch.max(answer, 1)[1].view(batch.label.size()) ==
            batch.label).sum()
        n_total += batch.batch_size
        train_acc = 100.0 * n_correct / n_total
        loss = criterion(answer, batch.label - 1)
        loss.backward()
        opt.step()
        if iterations % 5 == 0:
            model.eval()
            dev_iter.init_epoch()
            n_dev_correct, dev_loss = 0, 0
            for dev_batch_idx, dev_batch in enumerate(dev_iter):
                answer = model(dev_batch)
                n_dev_correct += (torch.max(answer, 1)[1].view(dev_batch.
                    label.size()) == dev_batch.label).sum()
                dev_loss = criterion(answer, dev_batch.label - 1)
                print(dev_loss.item())
            dev_acc = 100.0 * n_dev_correct / len(dev)

import os
import time
from pathlib import Path
from collections import namedtuple

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

from torchtext import data
from torchtext import datasets

from model import RNNClassifier

ConfigGen = namedtuple('ConfigGen', 'vocab_dim out_dim cells birnn dropout fc1_dim fc2_dim type n_layers embed_dim hidden_size')
ConfigGen.__new__.__defaults__ = (None,) * len(ConfigGen._fields)
USERHOME = str(Path.home())
batch_size = 64
inputs = data.Field(lower=True)
answers = data.Field(sequential=False)

train, dev, test = datasets.SNLI.splits(inputs, answers)

inputs.build_vocab(train, dev, test)
vector = os.path.join(USERHOME, '.vector_cache', 'glove.6B.300d.txt.pt')
if os.path.isfile(vector):
    # TODO - make it customizable
    inputs.vocab.vectors = torch.load(vector)
else:
    inputs.vocab.load_vectors('glove.6B.300d')
answers.build_vocab(train)

train_iter, dev_iter, test_iter = data.BucketIterator.splits(
    (train, dev, test), batch_size=batch_size)


vocab_dim = len(inputs.vocab)
out_dim = len(answers.vocab)
print(f'inputs.vocab = {inputs.vocab}')
print(f'answers.vocab = {answers.vocab}')

embed_dim = 300
cells = 2
birnn = True
lr = 0.01
epochs = 10
if birnn:
    cells *= 2
dropout = 0.5
fc1_dim = 50
fc2_dim = 3
n_layers = 2
network_type = 'LSTM'
hidden_size = 1000
config = ConfigGen(
    vocab_dim, out_dim, cells, birnn, dropout, fc1_dim, fc2_dim, network_type, n_layers, embed_dim, hidden_size)
model = RNNClassifier(config)
model.embed.weight.data = inputs.vocab.vectors
# TODO - convert to cuda if required

criterion = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=lr)

iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False

for epoch in range(epochs):
    train_iter.init_epoch()
    n_correct, n_total = 0, 0
    for batch_idx, batch in enumerate(train_iter):
        model.train()
        opt.zero_grad()
        iterations += 1
        answer = model(batch)
        n_correct += (torch.max(answer, 1)
                      [1].view(batch.label.size()) == batch.label).sum()
        n_total += batch.batch_size
        train_acc = 100. * n_correct / n_total
        # labels starts from 1 but we need it to start from 0
        loss = criterion(answer, batch.label - 1)
        loss.backward()
        opt.step()
        if iterations % 5 == 0:
            model.eval()
            dev_iter.init_epoch()
            n_dev_correct, dev_loss = 0, 0
            for dev_batch_idx, dev_batch in enumerate(dev_iter):
                answer = model(dev_batch)
                n_dev_correct += (torch.max(answer, 1)
                                  [1].view(dev_batch.label.size()) == dev_batch.label).sum()
                dev_loss = criterion(answer, dev_batch.label - 1)
                print(dev_loss.item())
            dev_acc = 100. * n_dev_correct / len(dev)