import keras
import os

from keras.layers import Input, Dense
from keras import Model


MAX_TOKEN_LEN = 100
MAX_CHAR_ORD = 1023

WORD_AE_INPUT_SIZE = 500


def load_word_ae() -> Model:
    if "word_ae" in os.listdir():
        word_ae = keras.models.load_model("word_ae")
        word_ae_encoder = Model(word_ae.layers[0], word_ae.layers[2])
    else:
        input_layer = Input(shape=(WORD_AE_INPUT_SIZE,))
        encoded = Dense(WORD_AE_INPUT_SIZE // 2, activation="relu")(input_layer)
        encoded = Dense(1, activation="relu")(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE // 2, activation="sigmoid")(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE, activation="sigmoid")(decoded)

        word_ae = Model(input_layer, decoded)
        word_ae_encoder = Model(input_layer, encoded)

        word_ae.compile(optimizer="adam", loss="binary_crossentropy")

    return word_ae, word_ae_encoder

def load_text_ae() -> Model:
    if "text_ae" in os.listdir():
        text_ae = keras.models.load_model("text_ae")
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation="relu")(input_layer)
        encoded = Dense(100, activation="relu")(encoded)
        decoded = Dense(2500, activation="sigmoid")(encoded)
        decoded = Dense(5000, activation="sigmoid")(decoded)

        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)

        text_ae.compile(optimizer="adam", loss="binary_crossentropy")

    return text_ae, text_ae_encoder

4db5a1ddfd1dd41ec697b5935cc869ffa0f5fa9a
<mask token>


def load_text_ae() ->Model:
    if 'text_ae' in os.listdir():
        text_ae = keras.models.load_model('text_ae')
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation='relu')(input_layer)
        encoded = Dense(100, activation='relu')(encoded)
        decoded = Dense(2500, activation='sigmoid')(encoded)
        decoded = Dense(5000, activation='sigmoid')(decoded)
        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)
        text_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return text_ae, text_ae_encoder

<mask token>


def load_word_ae() ->Model:
    if 'word_ae' in os.listdir():
        word_ae = keras.models.load_model('word_ae')
        word_ae_encoder = Model(word_ae.layers[0], word_ae.layers[2])
    else:
        input_layer = Input(shape=(WORD_AE_INPUT_SIZE,))
        encoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='relu')(input_layer
            )
        encoded = Dense(1, activation='relu')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='sigmoid')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE, activation='sigmoid')(decoded)
        word_ae = Model(input_layer, decoded)
        word_ae_encoder = Model(input_layer, encoded)
        word_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return word_ae, word_ae_encoder


def load_text_ae() ->Model:
    if 'text_ae' in os.listdir():
        text_ae = keras.models.load_model('text_ae')
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation='relu')(input_layer)
        encoded = Dense(100, activation='relu')(encoded)
        decoded = Dense(2500, activation='sigmoid')(encoded)
        decoded = Dense(5000, activation='sigmoid')(decoded)
        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)
        text_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return text_ae, text_ae_encoder

<mask token>
MAX_TOKEN_LEN = 100
MAX_CHAR_ORD = 1023
WORD_AE_INPUT_SIZE = 500


def load_word_ae() ->Model:
    if 'word_ae' in os.listdir():
        word_ae = keras.models.load_model('word_ae')
        word_ae_encoder = Model(word_ae.layers[0], word_ae.layers[2])
    else:
        input_layer = Input(shape=(WORD_AE_INPUT_SIZE,))
        encoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='relu')(input_layer
            )
        encoded = Dense(1, activation='relu')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='sigmoid')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE, activation='sigmoid')(decoded)
        word_ae = Model(input_layer, decoded)
        word_ae_encoder = Model(input_layer, encoded)
        word_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return word_ae, word_ae_encoder


def load_text_ae() ->Model:
    if 'text_ae' in os.listdir():
        text_ae = keras.models.load_model('text_ae')
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation='relu')(input_layer)
        encoded = Dense(100, activation='relu')(encoded)
        decoded = Dense(2500, activation='sigmoid')(encoded)
        decoded = Dense(5000, activation='sigmoid')(decoded)
        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)
        text_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return text_ae, text_ae_encoder

import keras
import os
from keras.layers import Input, Dense
from keras import Model
MAX_TOKEN_LEN = 100
MAX_CHAR_ORD = 1023
WORD_AE_INPUT_SIZE = 500


def load_word_ae() ->Model:
    if 'word_ae' in os.listdir():
        word_ae = keras.models.load_model('word_ae')
        word_ae_encoder = Model(word_ae.layers[0], word_ae.layers[2])
    else:
        input_layer = Input(shape=(WORD_AE_INPUT_SIZE,))
        encoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='relu')(input_layer
            )
        encoded = Dense(1, activation='relu')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE // 2, activation='sigmoid')(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE, activation='sigmoid')(decoded)
        word_ae = Model(input_layer, decoded)
        word_ae_encoder = Model(input_layer, encoded)
        word_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return word_ae, word_ae_encoder


def load_text_ae() ->Model:
    if 'text_ae' in os.listdir():
        text_ae = keras.models.load_model('text_ae')
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation='relu')(input_layer)
        encoded = Dense(100, activation='relu')(encoded)
        decoded = Dense(2500, activation='sigmoid')(encoded)
        decoded = Dense(5000, activation='sigmoid')(decoded)
        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)
        text_ae.compile(optimizer='adam', loss='binary_crossentropy')
    return text_ae, text_ae_encoder

import keras
import os

from keras.layers import Input, Dense
from keras import Model


MAX_TOKEN_LEN = 100
MAX_CHAR_ORD = 1023

WORD_AE_INPUT_SIZE = 500


def load_word_ae() -> Model:
    if "word_ae" in os.listdir():
        word_ae = keras.models.load_model("word_ae")
        word_ae_encoder = Model(word_ae.layers[0], word_ae.layers[2])
    else:
        input_layer = Input(shape=(WORD_AE_INPUT_SIZE,))
        encoded = Dense(WORD_AE_INPUT_SIZE // 2, activation="relu")(input_layer)
        encoded = Dense(1, activation="relu")(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE // 2, activation="sigmoid")(encoded)
        decoded = Dense(WORD_AE_INPUT_SIZE, activation="sigmoid")(decoded)

        word_ae = Model(input_layer, decoded)
        word_ae_encoder = Model(input_layer, encoded)

        word_ae.compile(optimizer="adam", loss="binary_crossentropy")

    return word_ae, word_ae_encoder

def load_text_ae() -> Model:
    if "text_ae" in os.listdir():
        text_ae = keras.models.load_model("text_ae")
        text_ae_encoder = Model(text_ae.layers[0], text_ae.layers[2])
    else:
        input_layer = Input(shape=(5000,))
        encoded = Dense(2500, activation="relu")(input_layer)
        encoded = Dense(100, activation="relu")(encoded)
        decoded = Dense(2500, activation="sigmoid")(encoded)
        decoded = Dense(5000, activation="sigmoid")(decoded)

        text_ae = Model(input_layer, decoded)
        text_ae_encoder = Model(input_layer, encoded)

        text_ae.compile(optimizer="adam", loss="binary_crossentropy")

    return text_ae, text_ae_encoder
