




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     lowerCAmelCase_					    = pd.read_csv("""sample_data.csv""", header=None)
     lowerCAmelCase_					    = df.shape[:1][0]
     # If you're using some other dataset input the target column
     lowerCAmelCase_					    = df.iloc[:, 1:2]
     lowerCAmelCase_					    = actual_data.values.reshape(len_data, 1)
     lowerCAmelCase_					    = MinMaxScaler().fit_transform(actual_data)
     lowerCAmelCase_					    = 10
     lowerCAmelCase_					    = 5
     lowerCAmelCase_					    = 20
     lowerCAmelCase_					    = len_data - periods * look_back
     lowerCAmelCase_					    = actual_data[:division]
     lowerCAmelCase_					    = actual_data[division - look_back :]
     lowerCAmelCase_    ,	lowerCAmelCase_					    = [], []
     lowerCAmelCase_    ,	lowerCAmelCase_					    = [], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     lowerCAmelCase_					    = np.array(train_x)
     lowerCAmelCase_					    = np.array(test_x)
     lowerCAmelCase_					    = np.array([list(i.ravel()) for i in train_y])
     lowerCAmelCase_					    = np.array([list(i.ravel()) for i in test_y])

     lowerCAmelCase_					    = Sequential()
     model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(1_28, 1)))
     model.add(Dense(forward_days))
     model.compile(loss="""mean_squared_error""", optimizer="""adam""")
     lowerCAmelCase_					    = model.fit(
         x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
     )
     lowerCAmelCase_					    = model.predict(x_test)







from abc import ABC, abstractmethod
from argparse import ArgumentParser






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      @abstractmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        raise NotImplementedError()







      @abstractmethod
      def 	__magic_name__(   self		):
        raise NotImplementedError()

