

'''simple docstring'''



import os


def       snake_case   (	) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							os.path.dirname(os.path.realpath(snake_case			)			)
    lowerCAmelCase 						=							os.path.join(snake_case					,		'triangle.txt'			)

    with open(snake_case			) as f:
        lowerCAmelCase 						=							f.readlines()

    lowerCAmelCase 						=							[]
    for line in triangle:
        lowerCAmelCase 						=							[]
        for number in line.strip().split(' '			):
            numbers_from_line.append(int(snake_case			)			)
        a.append(snake_case			)

    for i in range(1					,		len(snake_case			)			):
        for j in range(len(a[i]			)			):
            lowerCAmelCase 						=							a[i - 1][j] if j != len(a[i - 1]			) else 0
            lowerCAmelCase 						=							a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(snake_case					,		snake_case			)
    return max(a[-1]			)


if __name__ == "__main__":
     print(solution())












"""simple docstring"""





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

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

							_UpperCamelCase					      =   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')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




