import os
import zipfile
import gdown
import tensorflow as tf
from deepface.commons import functions

# --------------------------------
# dependency configuration

tf_version = int(tf.__version__.split(".", maxsplit=1)[0])

if tf_version == 1:
    from keras.models import Model, Sequential
    from keras.layers import (
        Convolution2D,
        LocallyConnected2D,
        MaxPooling2D,
        Flatten,
        Dense,
        Dropout,
    )
else:
    from tensorflow.keras.models import Model, Sequential
    from tensorflow.keras.layers import (
        Convolution2D,
        LocallyConnected2D,
        MaxPooling2D,
        Flatten,
        Dense,
        Dropout,
    )


# -------------------------------------
# pylint: disable=line-too-long


def loadModel(
    url="https://github.com/swghosh/DeepFace/releases/download/weights-vggface2-2d-aligned/VGGFace2_DeepFace_weights_val-0.9034.h5.zip",
):
    base_model = Sequential()
    base_model.add(
        Convolution2D(32, (11, 11), activation="relu", name="C1", input_shape=(152, 152, 3))
    )
    base_model.add(MaxPooling2D(pool_size=3, strides=2, padding="same", name="M2"))
    base_model.add(Convolution2D(16, (9, 9), activation="relu", name="C3"))
    base_model.add(LocallyConnected2D(16, (9, 9), activation="relu", name="L4"))
    base_model.add(LocallyConnected2D(16, (7, 7), strides=2, activation="relu", name="L5"))
    base_model.add(LocallyConnected2D(16, (5, 5), activation="relu", name="L6"))
    base_model.add(Flatten(name="F0"))
    base_model.add(Dense(4096, activation="relu", name="F7"))
    base_model.add(Dropout(rate=0.5, name="D0"))
    base_model.add(Dense(8631, activation="softmax", name="F8"))

    # ---------------------------------

    home = functions.get_deepface_home()

    if os.path.isfile(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5") != True:
        print("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")

        output = home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"

        gdown.download(url, output, quiet=False)

        # unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
        with zipfile.ZipFile(output, "r") as zip_ref:
            zip_ref.extractall(home + "/.deepface/weights/")

    base_model.load_weights(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5")

    # drop F8 and D0. F7 is the representation layer.
    deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)

    return deepface_model