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

			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])
			A__           =							np.array(train_x)
			A__           =							np.array(test_x)
			A__           =							np.array([list(i.ravel()) for i in train_y])
			A__           =							np.array([list(i.ravel()) for i in test_y])

			A__           =							Sequential()
			model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
			model.add(LSTM(64, input_shape=(128, 1)))
			model.add(Dense(forward_days))
			model.compile(loss="""mean_squared_error""", optimizer="""adam""")
			A__           =							model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			A__           =							model.predict(x_test)




def  _lowerCamelCase					(   a_				: str						,			a_				: str):
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:List[str]						  =     len(a_)
			lowerCamelCase						:int						  =     [[False for _ in range(m + 1)] for _ in range(n + 1)]
			lowerCamelCase						:Optional[Any]						  =     True
			for i in range(a_):
						for j in range(m + 1):
									if dp[i][j]:
												if j < m and a[i].upper() == b[j]:
															lowerCamelCase						:Any						  =     True
												if a[i].islower():
															lowerCamelCase						:List[str]						  =     True
			return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



