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

from .utils.logger import DummyLogger


class LayerInfo():
    def __init__(self):
        self.memory = 0.0
        self.ops = 0.0
        self.output = 0.0


class Layer(nn.Module):
    # Default layer arguments
    ACTIVATION = F.leaky_relu

    BATCH_NORM = True
    BATCH_NORM_TRAINING = False
    BATCH_NORM_DECAY = 0.95

    REGULARIZER = None

    PADDING = 'SAME'

    IS_TRAINING = False
    METRICS = False
    VERBOSE = 0
    LOGGER = DummyLogger()

    def __init__(self, activation, batch_norm):
        super(Layer, self).__init__()
        self.name = 'Layer'
        self.info = LayerInfo()

        # Preload default
        self.activation = Layer.ACTIVATION if activation == 0 else activation
        self.batch_norm = Layer.BATCH_NORM if batch_norm is None else batch_norm

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        output = input_data
        if self.activation is not None:
            output = self.activation(output)
        if self.batch_norm is not None:
            output = self.batch_norm(output)
        return output


class Conv2d(Layer):
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1,
                 activation=0, batch_norm=None, **kwargs):
        super(Conv2d, self).__init__(activation, batch_norm)

        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, **kwargs)
        self.batch_norm = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.01) if self.batch_norm else None

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        return super().forward(self.conv(input_data))