def __init__(self, X, y, batch_size, process_fn=None):
        """A `Sequence` implementation that returns balanced `y` by undersampling majority class.

        Args:
            X: The numpy array of inputs.
            y: The numpy array of targets.
            batch_size: The generator mini-batch size.
            process_fn: The preprocessing function to apply on `X`
        """
        self.X = X
        self.y = y
        self.batch_size = batch_size
        self.process_fn = process_fn or (lambda x: x)

        self.pos_indices = np.where(y == 1)[0]
        self.neg_indices = np.where(y == 0)[0]
        self.n = min(len(self.pos_indices), len(self.neg_indices))
        self._index_array = None 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def test_warnings():
    a = Input(shape=(3,), name='input_a')
    b = Input(shape=(3,), name='input_b')

    a_2 = Dense(4, name='dense_1')(a)
    dp = Dropout(0.5, name='dropout')
    b_2 = dp(b)

    model = Model([a, b], [a_2, b_2])

    optimizer = 'rmsprop'
    loss = 'mse'
    loss_weights = [1., 0.5]
    model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
                  sample_weight_mode=None)

    def gen_data(batch_sz):
        while True:
            yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
                   [np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])

    with pytest.warns(Warning) as w:
        out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
    warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
    assert warning_raised, 'No warning raised when using generator with processes.'

    with pytest.warns(None) as w:
        out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
    assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.' 

def test_context_switch():
    enqueuer = OrderedEnqueuer(DummySequence([3, 200, 200, 3]), use_multiprocessing=True)
    enqueuer2 = OrderedEnqueuer(DummySequence([3, 200, 200, 3], value=15), use_multiprocessing=True)
    enqueuer.start(3, 10)
    enqueuer2.start(3, 10)
    gen_output = enqueuer.get()
    gen_output2 = enqueuer2.get()
    acc = []
    for i in range(100):
        acc.append(next(gen_output)[0, 0, 0, 0])
    assert acc[-1] == 99
    # One epoch is completed so enqueuer will switch the Sequence

    acc = []
    for i in range(100):
        acc.append(next(gen_output2)[0, 0, 0, 0])
    assert acc[-1] == 99 * 15
    # One epoch has been completed so enqueuer2 will switch

    # Be sure that both Sequence were updated
    assert next(gen_output)[0, 0, 0, 0] == 0
    assert next(gen_output)[0, 0, 0, 0] == 5
    assert next(gen_output2)[0, 0, 0, 0] == 0
    assert next(gen_output2)[0, 0, 0, 0] == 15 * 5

    # Tear down everything
    enqueuer.stop()
    enqueuer2.stop() 

def __len__(self):
        """Number of batches in the Sequence."""
        return int(np.ceil(len(self.samples) / self.batch_size)) 

def __init__(self, X, y, batch_size, process_fn=None):
        """A `Sequence` implementation that can pre-process a mini-batch via `process_fn`

        Args:
            X: The numpy array of inputs.
            y: The numpy array of targets.
            batch_size: The generator mini-batch size.
            process_fn: The preprocessing function to apply on `X`
        """
        self.X = X
        self.y = y
        self.batch_size = batch_size
        self.process_fn = process_fn or (lambda x: x)