



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

						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__  :  Dict								=					np.array(train_x)
						A__  :  Any								=					np.array(test_x)
						A__  :  List[Any]								=					np.array([list(i.ravel()) for i in train_y])
						A__  :  List[str]								=					np.array([list(i.ravel()) for i in test_y])

						A__  :  List[Any]								=					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__  :  str								=					model.fit(
						    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
						)
						A__  :  int								=					model.predict(x_test)










from pathlib import Path

import fire


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     List[str]									= Path(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= Path(__UpperCamelCase )
		dest_dir.mkdir(exist_ok=__UpperCamelCase )
		for path in src_dir.iterdir():
				lowerCAmelCase_			:     Optional[Any]									= [x.rstrip() for x in list(path.open().readlines() )][:n]
				lowerCAmelCase_			:     List[str]									= dest_dir.joinpath(path.name )
				print(__UpperCamelCase )
				dest_path.open('''w''' ).write('''\n'''.join(__UpperCamelCase ) )


if __name__ == "__main__":
						fire.Fire(minify)





