from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2

resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING = 'channels_last'
MERGE_AXIS = -1


def one_side_pad(x):
    x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
    if IMAGE_ORDERING == 'channels_first':
        x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
    elif IMAGE_ORDERING == 'channels_last':
        x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
    return x


def identity_block(input_tensor, kernel_size, filters, stage, block):
    """The identity block is the block that has no conv layer at shortcut.
    # Arguments
        input_tensor: input tensor
        kernel_size: defualt 3, the kernel size of middle conv layer at main path
        filters: list of integers, the filterss of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names
    # Returns
        Output tensor for the block.
    """
    filters1, filters2, filters3 = filters

    if IMAGE_ORDERING == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
               padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = layers.add([x, input_tensor])
    x = Activation('relu')(x)
    return x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    """conv_block is the block that has a conv layer at shortcut
    # Arguments
        input_tensor: input tensor
        kernel_size: defualt 3, the kernel size of middle conv layer at main path
        filters: list of integers, the filterss of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names
    # Returns
        Output tensor for the block.
    Note that from stage 3, the first conv layer at main path is with strides=(2,2)
    And the shortcut should have strides=(2,2) as well
    """
    filters1, filters2, filters3 = filters

    if IMAGE_ORDERING == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
               name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same',
               name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
                      name=conv_name_base + '1')(input_tensor)
    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = layers.add([x, shortcut])
    x = Activation('relu')(x)
    return x


def resnet50_unet_light(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
    assert input_height % 32 == 0
    assert input_width % 32 == 0

    img_input = Input(shape=(input_height, input_width, 3))

    if IMAGE_ORDERING == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
    x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
               name='conv1')(x)
    f1 = x

    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
    f2 = one_side_pad(x)

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
    f3 = x

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
    f4 = x

    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
    f5 = x

    if pretraining:
        model = Model(img_input, x).load_weights(resnet50_Weights_path)

    v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
    v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
    v512_2048 = Activation('relu')(v512_2048)

    v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
    v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
    v512_1024 = Activation('relu')(v512_1024)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
    o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f3], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f2], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f1], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, img_input], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = (Activation('softmax'))(o)

    model = Model(img_input, o)
    return model


def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
    assert input_height % 32 == 0
    assert input_width % 32 == 0

    img_input = Input(shape=(input_height, input_width, 3))

    if IMAGE_ORDERING == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
    x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
               name='conv1')(x)
    f1 = x

    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
    f2 = one_side_pad(x)

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
    f3 = x

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
    f4 = x

    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
    f5 = x

    if pretraining:
        Model(img_input, x).load_weights(resnet50_Weights_path)

    v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
        f5)
    v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
    v1024_2048 = Activation('relu')(v1024_2048)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
    o = (concatenate([o, f4], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f3], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f2], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, f1], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
    o = (concatenate([o, img_input], axis=MERGE_AXIS))
    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
    o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = Activation('relu')(o)

    o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
    o = (BatchNormalization(axis=bn_axis))(o)
    o = (Activation('softmax'))(o)

    model = Model(img_input, o)

    return model