




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

					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_				:    Optional[Any]										=     np.array(test_x)
					a_				:    List[str]										=     np.array([list(i.ravel()) for i in train_y])
					a_				:    Any										=     np.array([list(i.ravel()) for i in test_y])

					a_				:    Union[str, Any]										=     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''')
					a_				:    Any										=     model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					a_				:    Union[str, Any]										=     model.predict(x_test)









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



