



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     :							Optional[Any]  							=				pd.read_csv("sample_data.csv", header=None)
		__lowerCamelCase     :							Any  							=				df.shape[:1][0]
		# If you're using some other dataset input the target column
		__lowerCamelCase     :							Optional[int]  							=				df.iloc[:, 1:2]
		__lowerCamelCase     :							Union[str, Any]  							=				actual_data.values.reshape(len_data, 1)
		__lowerCamelCase     :							Tuple  							=				MinMaxScaler().fit_transform(actual_data)
		__lowerCamelCase     :							Optional[Any]  							=				10
		__lowerCamelCase     :							Optional[Any]  							=				5
		__lowerCamelCase     :							Tuple  							=				20
		__lowerCamelCase     :							Any  							=				len_data - periods * look_back
		__lowerCamelCase     :							Tuple  							=				actual_data[:division]
		__lowerCamelCase     :							List[str]  							=				actual_data[division - look_back :]
		__lowerCamelCase							,    __lowerCamelCase     :							str  							=				[], []
		__lowerCamelCase							,    __lowerCamelCase     :							List[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])
		__lowerCamelCase     :							List[Any]  							=				np.array(train_x)
		__lowerCamelCase     :							Optional[int]  							=				np.array(test_x)
		__lowerCamelCase     :							int  							=				np.array([list(i.ravel()) for i in train_y])
		__lowerCamelCase     :							Union[str, Any]  							=				np.array([list(i.ravel()) for i in test_y])

		__lowerCamelCase     :							Union[str, 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")
		__lowerCamelCase     :							Tuple  							=				model.fit(
		    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
		)
		__lowerCamelCase     :							Optional[Any]  							=				model.predict(x_test)








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

						# Initialize Result
						UpperCAmelCase         =     []

						# Traverse through all denomination
						for denomination in reversed(lowerCamelCase_    ):
												# Find denominations
												while int(lowerCamelCase_    ) >= int(lowerCamelCase_    ):
																		total_value -= int(lowerCamelCase_    )
																		answer.append(lowerCamelCase_    )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				int(input("Enter the number of denominations you want to add: ").strip())

				for i in range(0, n):
						denominations.append(int(input(F'''Denomination {i}: ''').strip()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				input("Enter the change you want to make: ").strip()

		if int(value) == 0 or int(value) < 0:
				print("The total value cannot be zero or negative.")

		else:
				print(F'''Following is minimal change for {value}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



