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''' Unit tests for Gluon Batch Processor '''

import sys
import unittest
import warnings

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from mxnet.gluon.contrib.estimator import *
from mxnet.gluon.contrib.estimator.event_handler import *
from mxnet.gluon.contrib.estimator.batch_processor import BatchProcessor
import pytest

mx.npx.reset_np()

def _get_test_network():
    net = nn.Sequential()
    net.add(nn.Dense(4, activation='relu', flatten=False))
    return net


def _get_test_data():
    batch_size = 4
    in_data = mx.np.random.uniform(size=(10, 3))
    out_data = mx.np.random.uniform(size=(10, 4))
    # Input dataloader
    dataset = gluon.data.dataset.ArrayDataset(in_data, out_data)
    dataloader = gluon.data.DataLoader(dataset, batch_size=batch_size)
    dataiter = mx.io.NDArrayIter(data=in_data, label=out_data, batch_size=batch_size)
    return dataloader, dataiter

@mx.util.use_np
def test_batch_processor_fit():
    ''' test estimator with different train data types '''
    net = _get_test_network()
    dataloader, dataiter = _get_test_data()
    num_epochs = 1
    device = mx.cpu()
    loss = gluon.loss.L2Loss()
    acc = mx.gluon.metric.Accuracy()
    net.initialize(device=device)
    processor = BatchProcessor()
    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
    est = Estimator(net=net,
                    loss=loss,
                    train_metrics=acc,
                    trainer=trainer,
                    device=device,
                    batch_processor=processor)

    est.fit(train_data=dataloader,
            epochs=num_epochs)

    with pytest.raises(ValueError):
        est.fit(train_data=dataiter,
                epochs=num_epochs)

    # Input NDArray
    with pytest.raises(ValueError):
        est.fit(train_data=[mx.nd.ones(shape=(10, 3))],
                epochs=num_epochs)


@mx.util.use_np
def test_batch_processor_validation():
    ''' test different validation data types'''
    net = _get_test_network()
    dataloader, dataiter = _get_test_data()
    num_epochs = 1
    device = mx.cpu()
    loss = gluon.loss.L2Loss()
    acc = mx.gluon.metric.Accuracy()
    val_loss = gluon.loss.L1Loss()
    net.initialize(device=device)
    processor = BatchProcessor()
    trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
    est = Estimator(net=net,
                    loss=loss,
                    train_metrics=acc,
                    trainer=trainer,
                    device=device,
                    val_loss=val_loss,
                    batch_processor=processor)
    # Input dataloader
    est.fit(train_data=dataloader,
            val_data=dataloader,
            epochs=num_epochs)

    # using validation handler
    train_metrics = est.train_metrics
    val_metrics = est.val_metrics
    validation_handler = ValidationHandler(val_data=dataloader, eval_fn=est.evaluate)

    with pytest.raises(ValueError):
        est.fit(train_data=dataiter,
                val_data=dataiter,
                epochs=num_epochs)
    # Input NDArray
    with pytest.raises(ValueError):
        est.fit(train_data=[mx.nd.ones(shape=(10, 3))],
                val_data=[mx.nd.ones(shape=(10, 3))],
                epochs=num_epochs)