import tensorflow as tf
from typing import Tuple

def _inception_module(
    input_tensor,
    stride=1,
    activation="linear",
    use_bottleneck=True,
    kernel_size=40,
    bottleneck_size=32,
    nb_filters=32,
):

    if use_bottleneck and int(input_tensor.shape[-1]) > 1:
        input_inception = tf.keras.layers.Conv1D(
            filters=bottleneck_size,
            kernel_size=1,
            padding="same",
            activation=activation,
            use_bias=False,
        )(input_tensor)
    else:
        input_inception = input_tensor

    # kernel_size_s = [3, 5, 8, 11, 17]
    kernel_size_s = [kernel_size // (2**i) for i in range(3)]

    conv_list = []

    for i in range(len(kernel_size_s)):
        conv_list.append(
            tf.keras.layers.Conv1D(
                filters=nb_filters,
                kernel_size=kernel_size_s[i],
                strides=stride,
                padding="same",
                activation=activation,
                use_bias=False,
            )(input_inception)
        )

    max_pool_1 = tf.keras.layers.MaxPool1D(pool_size=3, strides=stride, padding="same")(
        input_tensor
    )

    conv_6 = tf.keras.layers.Conv1D(
        filters=nb_filters,
        kernel_size=1,
        padding="same",
        activation=activation,
        use_bias=False,
    )(max_pool_1)

    conv_list.append(conv_6)

    x = tf.keras.layers.Concatenate(axis=2)(conv_list)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation(activation="relu")(x)
    return x


def _shortcut_layer(input_tensor, out_tensor):
    shortcut_y = tf.keras.layers.Conv1D(
        filters=int(out_tensor.shape[-1]), kernel_size=1, padding="same", use_bias=False
    )(input_tensor)
    shortcut_y = tf.keras.layers.BatchNormalization()(shortcut_y)

    x = tf.keras.layers.Add()([shortcut_y, out_tensor])
    x = tf.keras.layers.Activation("relu")(x)
    return x


def build_age_model(
    input_shape: Tuple[int, int],
    nb_classes: int,
    depth: int = 6,
    use_residual: bool = True,
)-> tf.keras.models.Model:
    """
    Model proposed by HI Fawas et al 2019 "Finding AlexNet for Time Series Classification - InceptionTime"
    """
    input_layer = tf.keras.layers.Input(input_shape)

    x = input_layer
    input_res = input_layer

    for d in range(depth):

        x = _inception_module(x)

        if use_residual and d % 3 == 2:
            x = _shortcut_layer(input_res, x)
            input_res = x

    gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)

    output_layer = tf.keras.layers.Dense(units=nb_classes, activation="linear")(
        gap_layer
    )

    model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
    model.compile(
        loss=tf.keras.losses.MeanAbsoluteError(),
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
        metrics=[tf.keras.metrics.MeanSquaredError()],
    )

    return model



def build_gender_model(
    input_shape: Tuple[int, int],
    nb_classes: int,
    depth: int = 6,
    use_residual: bool = True,
)-> tf.keras.models.Model:
    """
    Model proposed by HI Fawas et al 2019 "Finding AlexNet for Time Series Classification - InceptionTime"
    """
    input_layer = tf.keras.layers.Input(input_shape)

    x = input_layer
    input_res = input_layer

    for d in range(depth):

        x = _inception_module(x)

        if use_residual and d % 3 == 2:
            x = _shortcut_layer(input_res, x)
            input_res = x

    gap_layer = tf.keras.layers.GlobalAveragePooling1D()(x)

    output_layer = tf.keras.layers.Dense(units=nb_classes, activation="sigmoid")(
        gap_layer
    )

    model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)
    model.compile(
        loss=tf.keras.losses.BinaryCrossentropy(),
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
        metrics=[tf.keras.metrics.AUC(curve='ROC',name="AUROC")],
    )

    return model