"""Darknet19 Model Defined in Keras."""
import functools
from functools import partial

from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2

from ..utils import compose

# Partial wrapper for Convolution2D with static default argument.
_DarknetConv2D = partial(Conv2D, padding='same')


@functools.wraps(Conv2D)
def DarknetConv2D(*args, **kwargs):
    """Wrapper to set Darknet weight regularizer for Convolution2D."""
    darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
    darknet_conv_kwargs.update(kwargs)
    return _DarknetConv2D(*args, **darknet_conv_kwargs)


def DarknetConv2D_BN_Leaky(*args, **kwargs):
    """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
    no_bias_kwargs = {'use_bias': False}
    no_bias_kwargs.update(kwargs)
    return compose(
        DarknetConv2D(*args, **no_bias_kwargs),
        BatchNormalization(),
        LeakyReLU(alpha=0.1))


def bottleneck_block(outer_filters, bottleneck_filters):
    """Bottleneck block of 3x3, 1x1, 3x3 convolutions."""
    return compose(
        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)),
        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))


def bottleneck_x2_block(outer_filters, bottleneck_filters):
    """Bottleneck block of 3x3, 1x1, 3x3, 1x1, 3x3 convolutions."""
    return compose(
        bottleneck_block(outer_filters, bottleneck_filters),
        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))


def darknet_body():
    """Generate first 18 conv layers of Darknet-19."""
    return compose(
        DarknetConv2D_BN_Leaky(32, (3, 3)),
        MaxPooling2D(),
        DarknetConv2D_BN_Leaky(64, (3, 3)),
        MaxPooling2D(),
        bottleneck_block(128, 64),
        MaxPooling2D(),
        bottleneck_block(256, 128),
        MaxPooling2D(),
        bottleneck_x2_block(512, 256),
        MaxPooling2D(),
        bottleneck_x2_block(1024, 512))


def darknet19(inputs):
    """Generate Darknet-19 model for Imagenet classification."""
    body = darknet_body()(inputs)
    logits = DarknetConv2D(1000, (1, 1), activation='softmax')(body)
    return Model(inputs, logits)
