"""Residual blocks: Conv and identity"""

from keras.initializers import glorot_uniform
from keras.layers import Add, Activation, Conv2D, BatchNormalization


def basic_block(X, f, filters, s, label):
    """
    Residual net identity block.
    
    Arguments:
    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
    f -- integer, specifying the shape of the middle CONV's window for the main path
    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
    label -- label to use for naming
    s -- strides
    
    Returns:
    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
    """
    F1, F2 = filters
    X_shortcut = X

    # First component of main path 
    X = Conv2D(
            F1,
            kernel_size=(f, f),
            strides=(s, s),
            padding='same',
            name='conv_%s_2a' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2a' % label)(X)
    X = Activation('relu')(X)

    # Second component of main path
    X = Conv2D(
            F2,
            kernel_size=(f, f),
            strides=(1, 1),
            padding='same',
            name='conv_%s_2b' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2b' % label)(X)

    # Shortcut path
    X_shortcut = Conv2D(
            F2,
            kernel_size=(1, 1),
            strides=(s, s),
            padding='valid',
            name='conv_%s_1' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X_shortcut)
    X_shortcut = BatchNormalization(axis=3, name='bn_%s_1' % label)(X_shortcut)

    # Add shortcut value to main path, and pass it through a RELU activation
    X = Add()([X_shortcut, X])
    X = Activation('relu')(X)
    
    return X


def identity_block(X, f, filters, label):
    """
    Residual net identity block.
    
    Arguments:
    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
    f -- integer, specifying the shape of the middle CONV's window for the main path
    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
    label -- label to use for naming
    
    Returns:
    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
    """
    F1, F2, F3 = filters
    X_shortcut = X

    # First component of main path 
    X = Conv2D(
            F1,
            kernel_size=(1, 1),
            strides=(1,1),
            padding='valid',
            name='conv_%s_2a' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2a' % label)(X)
    X = Activation('relu')(X)

    # Second component of main path
    X = Conv2D(
            F2,
            kernel_size=(f, f),
            strides=(1, 1),
            padding='same',
            name='conv_%s_2b' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2b' % label)(X)
    X = Activation('relu')(X)

    # Third component of main path
    X = Conv2D(
            F3,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding='valid',
            name='conv_%s_2c' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2c' % label)(X)

    # Add shortcut value to main path, and pass it through a RELU activation
    X = Add()([X_shortcut, X])
    X = Activation('relu')(X)
    
    return X


def convolutional_block(X, f, filters, label, s=2):
    """
    Residual net convolutional block.
    
    Arguments:
    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
    f -- integer, specifying the shape of the middle CONV's window for the main path
    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
    label -- label to use for naming
    s -- Integer, specifying the stride to be used
    
    Returns:
    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
    """
    F1, F2, F3 = filters
    X_shortcut = X

    # First component of main path 
    X = Conv2D(
            F1,
            kernel_size=(1, 1),
            strides=(s,s),
            padding='valid',
            name='conv_%s_2a' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2a' % label)(X)
    X = Activation('relu')(X)

    # Second component of main path
    X = Conv2D(
            F2,
            kernel_size=(f, f),
            strides=(1, 1),
            padding='same',
            name='conv_%s_2b' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2b' % label)(X)
    X = Activation('relu')(X)

    # Third component of main path
    X = Conv2D(
            F3,
            kernel_size=(1, 1),
            strides=(1, 1),
            padding='valid',
            name='conv_%s_2c' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X)
    X = BatchNormalization(axis=3, name='bn_%s_2c' % label)(X)

    # Shortcut path
    X_shortcut = Conv2D(
            F3,
            kernel_size=(1, 1),
            strides=(s, s),
            padding='valid',
            name='conv_%s_1' % label,
            kernel_initializer=glorot_uniform(seed=0)
    )(X_shortcut)
    X_shortcut = BatchNormalization(axis=3, name='bn_%s_1' % label)(X_shortcut)

    # Add shortcut value to main path, and pass it through a RELU activation
    X = Add()([X_shortcut, X])
    X = Activation('relu')(X)

    return X