from paddle import fluid
import paddle.fluid.layers as F
import paddle.fluid.dygraph as dg


class Pool1D(dg.Layer):
    """
    A Pool 1D block implemented with Pool2D.
    """
    def __init__(self,
                 pool_size=-1, 
                 pool_type='max', 
                 pool_stride=1, 
                 pool_padding=0, 
                 global_pooling=False, 
                 use_cudnn=True, 
                 ceil_mode=False, 
                 exclusive=True,
                 data_format='NCT'):
        super(Pool1D, self).__init__()
        self.pool_size = pool_size
        self.pool_type = pool_type
        self.pool_stride = pool_stride
        self.pool_padding = pool_padding
        self.global_pooling = global_pooling
        self.use_cudnn = use_cudnn
        self.ceil_mode = ceil_mode
        self.exclusive = exclusive
        self.data_format = data_format


        self.pool2d = dg.Pool2D([1,pool_size], pool_type = pool_type,
                                pool_stride = [1,pool_stride], pool_padding = [0, pool_padding],
                                global_pooling = global_pooling, use_cudnn = use_cudnn,
                                ceil_mode = ceil_mode, exclusive = exclusive)

    
    def forward(self, x):
        """
        Args:
            x (Variable): Shape(B, C_in, 1, T), the input, where C_in means
                input channels.
        Returns:
            x (Variable): Shape(B, C_out, 1, T), the outputs, where C_out means
                output channels (num_filters).
        """
        if self.data_format == 'NTC':
            x = fluid.layers.transpose(x, [0, 2, 1])
        x = fluid.layers.unsqueeze(x, [2])
        x = self.pool2d(x)
        x = fluid.layers.squeeze(x, [2])
        if self.data_format == 'NTC':
            x = fluid.layers.transpose(x, [0, 2, 1])
        return x

class Conv1D(dg.Conv2D):
    """A standard Conv1D layer that use (B, C, T) data layout. It inherit Conv2D and 
    use (B, C, 1, T) data layout to compute 1D convolution. Nothing more.
    NOTE: we inherit Conv2D instead of encapsulate a Conv2D layer to make it a simple
    layer, instead of a complex one. So we can easily apply weight norm to it.
    """
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        super(Conv1D, self).__init__(num_channels,
                                     num_filters, (1, filter_size),
                                     stride=(1, stride),
                                     padding=(0, padding),
                                     dilation=(1, dilation),
                                     groups=groups,
                                     param_attr=param_attr,
                                     bias_attr=bias_attr,
                                     use_cudnn=use_cudnn,
                                     act=act,
                                     dtype=dtype)

    def forward(self, x):
        x = F.unsqueeze(x, [2])
        x = super(Conv1D, self).forward(x)  # maybe risky here
        x = F.squeeze(x, [2])
        return x


class Conv1DTranspose(dg.Conv2DTranspose):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 padding=0,
                 stride=1,
                 dilation=1,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        super(Conv1DTranspose, self).__init__(num_channels,
                                              num_filters, (1, filter_size),
                                              output_size=None,
                                              padding=(0, padding),
                                              stride=(1, stride),
                                              dilation=(1, dilation),
                                              groups=groups,
                                              param_attr=param_attr,
                                              bias_attr=bias_attr,
                                              use_cudnn=use_cudnn,
                                              act=act,
                                              dtype=dtype)

    def forward(self, x):
        x = F.unsqueeze(x, [2])
        x = super(Conv1DTranspose, self).forward(x)  # maybe risky here
        x = F.squeeze(x, [2])
        return x


class Conv1DCell(Conv1D):
    """A causal convolve-1d cell. It uses causal padding, padding(receptive_field -1,  0).
    But Conv2D in dygraph does not support asymmetric padding yet, we just pad
    (receptive_field -1, receptive_field -1) and drop last receptive_field -1 steps in 
    the output.
    
    It is a cell that it acts like an RNN cell. It does not support stride > 1, and it
    ensures 1-to-1 mapping from input time steps to output timesteps.
    """
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 dilation=1,
                 causal=False,
                 groups=None,
                 param_attr=None,
                 bias_attr=None,
                 use_cudnn=True,
                 act=None,
                 dtype='float32'):
        receptive_field = 1 + dilation * (filter_size - 1)
        padding = receptive_field - 1 if causal else receptive_field // 2
        self._receptive_field = receptive_field
        self.causal = causal
        super(Conv1DCell, self).__init__(num_channels,
                                         num_filters,
                                         filter_size,
                                         stride=1,
                                         padding=padding,
                                         dilation=dilation,
                                         groups=groups,
                                         param_attr=param_attr,
                                         bias_attr=bias_attr,
                                         use_cudnn=use_cudnn,
                                         act=act,
                                         dtype=dtype)

    def forward(self, x):
        # it ensures that ouput time steps == input time steps
        time_steps = x.shape[-1]
        x = super(Conv1DCell, self).forward(x)
        if x.shape[-1] != time_steps:
            x = x[:, :, :time_steps]
        return x

    @property
    def receptive_field(self):
        return self._receptive_field

    def start_sequence(self):
        if not self.causal:
            raise ValueError(
                "Only causal conv1d shell should use start sequence")
        if self.receptive_field == 1:
            raise ValueError(
                "Convolution block with receptive field = 1 does not need"
                " to be implemented as a Conv1DCell. Conv1D suffices")
        self._buffer = None
        self._reshaped_weight = F.reshape(self.weight, (self._num_filters, -1))

    def add_input(self, x_t):
        batch_size, c_in, _ = x_t.shape
        if self._buffer is None:
            self._buffer = F.zeros((batch_size, c_in, self.receptive_field),
                                   dtype=x_t.dtype)
        self._buffer = F.concat([self._buffer[:, :, 1:], x_t], -1)
        if self._dilation[1] > 1:
            input = F.strided_slice(self._buffer,
                                    axes=[2],
                                    starts=[0],
                                    ends=[self.receptive_field],
                                    strides=[self._dilation[1]])
        else:
            input = self._buffer
        input = F.reshape(input, (batch_size, -1))
        y_t = F.matmul(input, self._reshaped_weight, transpose_y=True)
        y_t = y_t + self.bias
        y_t = F.unsqueeze(y_t, [-1])
        return y_t