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import torch
import torch.nn as nn
import torch.nn.functional as F


class ConvLSTMCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size, bias, device):
        """
        Initialize ConvLSTM cell.
        Parameters
        ----------
        input_dim: int
            Number of channels of input tensor.
        hidden_dim: int
            Number of channels of hidden state.
        kernel_size: (int, int)
            Size of the convolutional kernel.
        bias: bool
            Whether or not to add the bias.
        """

        super(ConvLSTMCell, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim

        self.kernel_size = kernel_size
        self.padding = kernel_size[0] // 2, kernel_size[1] // 2
        self.bias = bias
        self.device = device

        self.conv = nn.Conv2d(
            in_channels=self.input_dim + self.hidden_dim,
            out_channels=4 * self.hidden_dim,
            kernel_size=self.kernel_size,
            padding=self.padding,
            bias=self.bias,
        )

    def __initStates(self, size):
        return torch.zeros(size).to(self.device), torch.zeros(size).to(self.device)
        # return torch.zeros(size).cuda(), torch.zeros(size).cuda()

    def forward(self, input_tensor, cur_state):
        if cur_state == None:
            h_cur, c_cur = self.__initStates(
                [
                    input_tensor.shape[0],
                    self.hidden_dim,
                    input_tensor.shape[2],
                    input_tensor.shape[3],
                ]
            )
        else:
            h_cur, c_cur = cur_state

        combined = torch.cat(
            [input_tensor, h_cur], dim=1
        )  # concatenate along channel axis
        combined_conv = self.conv(combined)
        cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)

        i = torch.sigmoid(cc_i)
        f = torch.sigmoid(cc_f)
        o = torch.sigmoid(cc_o)
        g = torch.tanh(cc_g)

        c_next = f * c_cur + i * g
        h_next = o * torch.tanh(c_next)

        return h_next, c_next

    def init_hidden(self, batch_size, image_size):
        height, width = image_size
        return (
            torch.zeros(
                batch_size,
                self.hidden_dim,
                height,
                width,
                device=self.conv.weight.device,
            ),
            torch.zeros(
                batch_size,
                self.hidden_dim,
                height,
                width,
                device=self.conv.weight.device,
            ),
        )


class ConvLSTM(nn.Module):
    """
    Parameters:
        input_dim: Number of channels in input
        hidden_dim: Number of hidden channels
        kernel_size: Size of kernel in convolutions
        num_layers: Number of LSTM layers stacked on each other
        batch_first: Whether or not dimension 0 is the batch or not
        bias: Bias or no bias in Convolution
        return_all_layers: Return the list of computations for all layers
        Note: Will do same padding.
    Input:
        A tensor of size B, T, C, H, W or T, B, C, H, W
    Output:
        A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
            0 - layer_output_list is the list of lists of length T of each output
            1 - last_state_list is the list of last states
                    each element of the list is a tuple (h, c) for hidden state and memory
    Example:
        >> x = torch.rand((32, 10, 64, 128, 128))
        >> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
        >> _, last_states = convlstm(x)
        >> h = last_states[0][0]  # 0 for layer index, 0 for h index
    """

    def __init__(
        self,
        input_dim,
        hidden_dim,
        kernel_size,
        num_layers,
        batch_first=False,
        bias=True,
        return_all_layers=False,
    ):
        super(ConvLSTM, self).__init__()

        self._check_kernel_size_consistency(kernel_size)

        # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
        kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
        hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
        if not len(kernel_size) == len(hidden_dim) == num_layers:
            raise ValueError("Inconsistent list length.")

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.bias = bias
        self.return_all_layers = return_all_layers

        cell_list = []
        for i in range(0, self.num_layers):
            cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]

            cell_list.append(
                ConvLSTMCell(
                    input_dim=cur_input_dim,
                    hidden_dim=self.hidden_dim[i],
                    kernel_size=self.kernel_size[i],
                    bias=self.bias,
                )
            )

        self.cell_list = nn.ModuleList(cell_list)

    def forward(self, input_tensor, hidden_state=None):
        """
        Parameters
        ----------
        input_tensor: todo
            5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
        hidden_state: todo
            None. todo implement stateful
        Returns
        -------
        last_state_list, layer_output
        """
        if not self.batch_first:
            # (t, b, c, h, w) -> (b, t, c, h, w)
            input_tensor = input_tensor.permute(1, 0, 2, 3, 4)

        b, _, _, h, w = input_tensor.size()

        # Implement stateful ConvLSTM
        if hidden_state is not None:
            raise NotImplementedError()
        else:
            # Since the init is done in forward. Can send image size here
            hidden_state = self._init_hidden(batch_size=b, image_size=(h, w))

        layer_output_list = []
        last_state_list = []

        seq_len = input_tensor.size(1)
        cur_layer_input = input_tensor

        for layer_idx in range(self.num_layers):

            h, c = hidden_state[layer_idx]
            output_inner = []
            for t in range(seq_len):
                h, c = self.cell_list[layer_idx](
                    input_tensor=cur_layer_input[:, t, :, :, :], cur_state=[h, c]
                )
                output_inner.append(h)

            layer_output = torch.stack(output_inner, dim=1)
            cur_layer_input = layer_output

            layer_output_list.append(layer_output)
            last_state_list.append([h, c])

        if not self.return_all_layers:
            layer_output_list = layer_output_list[-1:]
            last_state_list = last_state_list[-1:]

        return layer_output_list, last_state_list

    def _init_hidden(self, batch_size, image_size):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
        return init_states

    @staticmethod
    def _check_kernel_size_consistency(kernel_size):
        if not (
            isinstance(kernel_size, tuple)
            or (
                isinstance(kernel_size, list)
                and all([isinstance(elem, tuple) for elem in kernel_size])
            )
        ):
            raise ValueError("`kernel_size` must be tuple or list of tuples")

    @staticmethod
    def _extend_for_multilayer(param, num_layers):
        if not isinstance(param, list):
            param = [param] * num_layers
        return param


def normal_init(m, mean, std):
    if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
        m.weight.data.normal_(mean, std)
        m.bias.data.zero_()


class Generator(nn.Module):
    def __init__(self, device, inputChannels=4, outputChannels=3, d=64):
        super().__init__()
        self.d = d
        self.device = device

        self.conv1 = nn.Conv2d(inputChannels, d, 3, 2, 1)
        self.conv2 = nn.Conv2d(d, d * 2, 3, 2, 1)
        self.conv3 = nn.Conv2d(d * 2, d * 4, 3, 2, 1)
        self.conv4 = nn.Conv2d(d * 4, d * 8, 3, 2, 1)
        self.conv5 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
        self.conv6 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
        self.conv7 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)

        self.conv_lstm_d1 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
        self.conv_lstm_d2 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
        self.conv_lstm_d3 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
        self.conv_lstm_d4 = ConvLSTMCell(d * 8 * 2, d * 4, (3, 3), False, device)
        self.conv_lstm_d5 = ConvLSTMCell(d * 4 * 2, d * 2, (3, 3), False, device)
        self.conv_lstm_d6 = ConvLSTMCell(d * 2 * 2, d, (3, 3), False, device)
        self.conv_lstm_d7 = ConvLSTMCell(d * 2, d, (3, 3), False, device)

        self.conv_lstm_e1 = ConvLSTMCell(d, d, (3, 3), False, device)
        self.conv_lstm_e2 = ConvLSTMCell(d * 2, d * 2, (3, 3), False, device)
        self.conv_lstm_e3 = ConvLSTMCell(d * 4, d * 4, (3, 3), False, device)
        self.conv_lstm_e4 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
        self.conv_lstm_e5 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
        self.conv_lstm_e6 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
        self.conv_lstm_e7 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)

        self.up = nn.Upsample(scale_factor=2)
        self.conv_out = nn.Conv2d(d, outputChannels, 3, 1, 1)

        self.slope = 0.2

    def weight_init(self, mean, std):
        for m in self._modules:
            normal_init(self._modules[m], mean, std)

    def forward_step(self, input, states_encoder, states_decoder):

        e1 = self.conv1(input)
        states_e1 = self.conv_lstm_e1(e1, states_encoder[0])
        e2 = self.conv2(F.leaky_relu(states_e1[0], self.slope))
        states_e2 = self.conv_lstm_e2(e2, states_encoder[1])
        e3 = self.conv3(F.leaky_relu(states_e2[0], self.slope))
        states_e3 = self.conv_lstm_e3(e3, states_encoder[2])
        e4 = self.conv4(F.leaky_relu(states_e3[0], self.slope))
        states_e4 = self.conv_lstm_e4(e4, states_encoder[3])
        e5 = self.conv5(F.leaky_relu(states_e4[0], self.slope))
        states_e5 = self.conv_lstm_e5(e5, states_encoder[4])
        e6 = self.conv6(F.leaky_relu(states_e5[0], self.slope))
        states_e6 = self.conv_lstm_e6(e6, states_encoder[5])
        e7 = self.conv7(F.leaky_relu(states_e6[0], self.slope))

        states1 = self.conv_lstm_d1(F.relu(e7), states_decoder[0])
        d1 = self.up(states1[0])
        d1 = torch.cat([d1, e6], 1)

        states2 = self.conv_lstm_d2(F.relu(d1), states_decoder[1])
        d2 = self.up(states2[0])
        d2 = torch.cat([d2, e5], 1)

        states3 = self.conv_lstm_d3(F.relu(d2), states_decoder[2])
        d3 = self.up(states3[0])
        d3 = torch.cat([d3, e4], 1)

        states4 = self.conv_lstm_d4(F.relu(d3), states_decoder[3])
        d4 = self.up(states4[0])
        d4 = torch.cat([d4, e3], 1)

        states5 = self.conv_lstm_d5(F.relu(d4), states_decoder[4])
        d5 = self.up(states5[0])
        d5 = torch.cat([d5, e2], 1)

        states6 = self.conv_lstm_d6(F.relu(d5), states_decoder[5])
        d6 = self.up(states6[0])
        d6 = torch.cat([d6, e1], 1)

        states7 = self.conv_lstm_d7(F.relu(d6), states_decoder[6])
        d7 = self.up(states7[0])

        o = torch.clip(torch.tanh(self.conv_out(d7)), min=-0.0, max=1)

        states_e = [states_e1, states_e2, states_e3, states_e4, states_e5, states_e6]
        states_d = [states1, states2, states3, states4, states5, states6, states7]

        return o, (states_e, states_d)

    def forward(self, tensor):
        states_encoder = (None, None, None, None, None, None, None)
        states_decoder = (None, None, None, None, None, None, None)
        output = torch.empty_like(tensor)
        for timeStep in range(tensor.shape[4]):
            output[:, :, :, :, timeStep], states = self.forward_step(
                tensor[:, :, :, :, timeStep], states_encoder, states_decoder
            )
            states_encoder, states_decoder = states[0], states[1]
        return output, states

	import torch
	import torch.nn as nn
	import torch.nn.functional as F


	class ConvLSTMCell(nn.Module):

	def __init__(self, input_dim, hidden_dim, kernel_size, bias, device):
	"""
	Initialize ConvLSTM cell.

	Parameters
	----------
	input_dim: int
	Number of channels of input tensor.
	hidden_dim: int
	Number of channels of hidden state.
	kernel_size: (int, int)
	Size of the convolutional kernel.
	bias: bool
	Whether or not to add the bias.
	"""

	super(ConvLSTMCell, self).__init__()

	self.input_dim = input_dim
	self.hidden_dim = hidden_dim

	self.kernel_size = kernel_size
	self.padding = kernel_size[0] // 2, kernel_size[1] // 2
	self.bias = bias
	self.device = device

	self.conv = nn.Conv2d(
	in_channels=self.input_dim + self.hidden_dim,
	out_channels=4 * self.hidden_dim,
	kernel_size=self.kernel_size,
	padding=self.padding,
	bias=self.bias,
	)

	def __initStates(self, size):
	return torch.zeros(size).to(self.device), torch.zeros(size).to(self.device)
	# return torch.zeros(size).cuda(), torch.zeros(size).cuda()

	def forward(self, input_tensor, cur_state):
	if cur_state == None:
	h_cur, c_cur = self.__initStates(
	[
	input_tensor.shape[0],
	self.hidden_dim,
	input_tensor.shape[2],
	input_tensor.shape[3],
	]
	)
	else:
	h_cur, c_cur = cur_state

	combined = torch.cat(
	[input_tensor, h_cur], dim=1
	) # concatenate along channel axis
	combined_conv = self.conv(combined)
	cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)

	i = torch.sigmoid(cc_i)
	f = torch.sigmoid(cc_f)
	o = torch.sigmoid(cc_o)
	g = torch.tanh(cc_g)

	c_next = f * c_cur + i * g
	h_next = o * torch.tanh(c_next)

	return h_next, c_next

	def init_hidden(self, batch_size, image_size):
	height, width = image_size
	return (
	torch.zeros(
	batch_size,
	self.hidden_dim,
	height,
	width,
	device=self.conv.weight.device,
	),
	torch.zeros(
	batch_size,
	self.hidden_dim,
	height,
	width,
	device=self.conv.weight.device,
	),
	)


	class ConvLSTM(nn.Module):
	"""

	Parameters:
	input_dim: Number of channels in input
	hidden_dim: Number of hidden channels
	kernel_size: Size of kernel in convolutions
	num_layers: Number of LSTM layers stacked on each other
	batch_first: Whether or not dimension 0 is the batch or not
	bias: Bias or no bias in Convolution
	return_all_layers: Return the list of computations for all layers
	Note: Will do same padding.

	Input:
	A tensor of size B, T, C, H, W or T, B, C, H, W
	Output:
	A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
	0 - layer_output_list is the list of lists of length T of each output
	1 - last_state_list is the list of last states
	each element of the list is a tuple (h, c) for hidden state and memory
	Example:
	>> x = torch.rand((32, 10, 64, 128, 128))
	>> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
	>> _, last_states = convlstm(x)
	>> h = last_states[0][0] # 0 for layer index, 0 for h index
	"""

	def __init__(
	self,
	input_dim,
	hidden_dim,
	kernel_size,
	num_layers,
	batch_first=False,
	bias=True,
	return_all_layers=False,
	):
	super(ConvLSTM, self).__init__()

	self._check_kernel_size_consistency(kernel_size)

	# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
	kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
	hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
	if not len(kernel_size) == len(hidden_dim) == num_layers:
	raise ValueError("Inconsistent list length.")

	self.input_dim = input_dim
	self.hidden_dim = hidden_dim
	self.kernel_size = kernel_size
	self.num_layers = num_layers
	self.batch_first = batch_first
	self.bias = bias
	self.return_all_layers = return_all_layers

	cell_list = []
	for i in range(0, self.num_layers):
	cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]

	cell_list.append(
	ConvLSTMCell(
	input_dim=cur_input_dim,
	hidden_dim=self.hidden_dim[i],
	kernel_size=self.kernel_size[i],
	bias=self.bias,
	)
	)

	self.cell_list = nn.ModuleList(cell_list)

	def forward(self, input_tensor, hidden_state=None):
	"""

	Parameters
	----------
	input_tensor: todo
	5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
	hidden_state: todo
	None. todo implement stateful

	Returns
	-------
	last_state_list, layer_output
	"""
	if not self.batch_first:
	# (t, b, c, h, w) -> (b, t, c, h, w)
	input_tensor = input_tensor.permute(1, 0, 2, 3, 4)

	b, _, _, h, w = input_tensor.size()

	# Implement stateful ConvLSTM
	if hidden_state is not None:
	raise NotImplementedError()
	else:
	# Since the init is done in forward. Can send image size here
	hidden_state = self._init_hidden(batch_size=b, image_size=(h, w))

	layer_output_list = []
	last_state_list = []

	seq_len = input_tensor.size(1)
	cur_layer_input = input_tensor

	for layer_idx in range(self.num_layers):

	h, c = hidden_state[layer_idx]
	output_inner = []
	for t in range(seq_len):
	h, c = self.cell_list[layer_idx](
	input_tensor=cur_layer_input[:, t, :, :, :], cur_state=[h, c]
	)
	output_inner.append(h)

	layer_output = torch.stack(output_inner, dim=1)
	cur_layer_input = layer_output

	layer_output_list.append(layer_output)
	last_state_list.append([h, c])

	if not self.return_all_layers:
	layer_output_list = layer_output_list[-1:]
	last_state_list = last_state_list[-1:]

	return layer_output_list, last_state_list

	def _init_hidden(self, batch_size, image_size):
	init_states = []
	for i in range(self.num_layers):
	init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
	return init_states

	@staticmethod
	def _check_kernel_size_consistency(kernel_size):
	if not (
	isinstance(kernel_size, tuple)
	or (
	isinstance(kernel_size, list)
	and all([isinstance(elem, tuple) for elem in kernel_size])
	)
	):
	raise ValueError("`kernel_size` must be tuple or list of tuples")

	@staticmethod
	def _extend_for_multilayer(param, num_layers):
	if not isinstance(param, list):
	param = [param] * num_layers
	return param


	def normal_init(m, mean, std):
	if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
	m.weight.data.normal_(mean, std)
	m.bias.data.zero_()


	class Generator(nn.Module):
	def __init__(self, device, inputChannels=4, outputChannels=3, d=64):
	super().__init__()
	self.d = d
	self.device = device

	self.conv1 = nn.Conv2d(inputChannels, d, 3, 2, 1)
	self.conv2 = nn.Conv2d(d, d * 2, 3, 2, 1)
	self.conv3 = nn.Conv2d(d * 2, d * 4, 3, 2, 1)
	self.conv4 = nn.Conv2d(d * 4, d * 8, 3, 2, 1)
	self.conv5 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
	self.conv6 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)
	self.conv7 = nn.Conv2d(d * 8, d * 8, 3, 2, 1)

	self.conv_lstm_d1 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
	self.conv_lstm_d2 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
	self.conv_lstm_d3 = ConvLSTMCell(d * 8 * 2, d * 8, (3, 3), False, device)
	self.conv_lstm_d4 = ConvLSTMCell(d * 8 * 2, d * 4, (3, 3), False, device)
	self.conv_lstm_d5 = ConvLSTMCell(d * 4 * 2, d * 2, (3, 3), False, device)
	self.conv_lstm_d6 = ConvLSTMCell(d * 2 * 2, d, (3, 3), False, device)
	self.conv_lstm_d7 = ConvLSTMCell(d * 2, d, (3, 3), False, device)

	self.conv_lstm_e1 = ConvLSTMCell(d, d, (3, 3), False, device)
	self.conv_lstm_e2 = ConvLSTMCell(d * 2, d * 2, (3, 3), False, device)
	self.conv_lstm_e3 = ConvLSTMCell(d * 4, d * 4, (3, 3), False, device)
	self.conv_lstm_e4 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
	self.conv_lstm_e5 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
	self.conv_lstm_e6 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)
	self.conv_lstm_e7 = ConvLSTMCell(d * 8, d * 8, (3, 3), False, device)

	self.up = nn.Upsample(scale_factor=2)
	self.conv_out = nn.Conv2d(d, outputChannels, 3, 1, 1)

	self.slope = 0.2

	def weight_init(self, mean, std):
	for m in self._modules:
	normal_init(self._modules[m], mean, std)

	def forward_step(self, input, states_encoder, states_decoder):

	e1 = self.conv1(input)
	states_e1 = self.conv_lstm_e1(e1, states_encoder[0])
	e2 = self.conv2(F.leaky_relu(states_e1[0], self.slope))
	states_e2 = self.conv_lstm_e2(e2, states_encoder[1])
	e3 = self.conv3(F.leaky_relu(states_e2[0], self.slope))
	states_e3 = self.conv_lstm_e3(e3, states_encoder[2])
	e4 = self.conv4(F.leaky_relu(states_e3[0], self.slope))
	states_e4 = self.conv_lstm_e4(e4, states_encoder[3])
	e5 = self.conv5(F.leaky_relu(states_e4[0], self.slope))
	states_e5 = self.conv_lstm_e5(e5, states_encoder[4])
	e6 = self.conv6(F.leaky_relu(states_e5[0], self.slope))
	states_e6 = self.conv_lstm_e6(e6, states_encoder[5])
	e7 = self.conv7(F.leaky_relu(states_e6[0], self.slope))

	states1 = self.conv_lstm_d1(F.relu(e7), states_decoder[0])
	d1 = self.up(states1[0])
	d1 = torch.cat([d1, e6], 1)

	states2 = self.conv_lstm_d2(F.relu(d1), states_decoder[1])
	d2 = self.up(states2[0])
	d2 = torch.cat([d2, e5], 1)

	states3 = self.conv_lstm_d3(F.relu(d2), states_decoder[2])
	d3 = self.up(states3[0])
	d3 = torch.cat([d3, e4], 1)

	states4 = self.conv_lstm_d4(F.relu(d3), states_decoder[3])
	d4 = self.up(states4[0])
	d4 = torch.cat([d4, e3], 1)

	states5 = self.conv_lstm_d5(F.relu(d4), states_decoder[4])
	d5 = self.up(states5[0])
	d5 = torch.cat([d5, e2], 1)

	states6 = self.conv_lstm_d6(F.relu(d5), states_decoder[5])
	d6 = self.up(states6[0])
	d6 = torch.cat([d6, e1], 1)

	states7 = self.conv_lstm_d7(F.relu(d6), states_decoder[6])
	d7 = self.up(states7[0])

	o = torch.clip(torch.tanh(self.conv_out(d7)), min=-0.0, max=1)

	states_e = [states_e1, states_e2, states_e3, states_e4, states_e5, states_e6]
	states_d = [states1, states2, states3, states4, states5, states6, states7]

	return o, (states_e, states_d)

	def forward(self, tensor):
	states_encoder = (None, None, None, None, None, None, None)
	states_decoder = (None, None, None, None, None, None, None)
	output = torch.empty_like(tensor)
	for timeStep in range(tensor.shape[4]):
	output[:, :, :, :, timeStep], states = self.forward_step(
	tensor[:, :, :, :, timeStep], states_encoder, states_decoder
	)
	states_encoder, states_decoder = states[0], states[1]
	return output, states