



"""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__":
		_lowercase			:			Dict										=						pd.read_csv("sample_data.csv", header=None)
		_lowercase			:			Union[str, Any]										=						df.shape[:1][0]
		# If you're using some other dataset input the target column
		_lowercase			:			Any										=						df.iloc[:, 1:2]
		_lowercase			:			Tuple										=						actual_data.values.reshape(len_data, 1)
		_lowercase			:			Optional[Any]										=						MinMaxScaler().fit_transform(actual_data)
		_lowercase			:			Tuple										=						1_0
		_lowercase			:			List[str]										=						5
		_lowercase			:			Union[str, Any]										=						2_0
		_lowercase			:			List[Any]										=						len_data - periods * look_back
		_lowercase			:			Dict										=						actual_data[:division]
		_lowercase			:			Optional[int]										=						actual_data[division - look_back :]
		_lowercase						,     _lowercase			:			Any										=						[], []
		_lowercase						,     _lowercase			:			Union[str, 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])
		_lowercase			:			Dict										=						np.array(train_x)
		_lowercase			:			Optional[int]										=						np.array(test_x)
		_lowercase			:			Optional[int]										=						np.array([list(i.ravel()) for i in train_y])
		_lowercase			:			List[Any]										=						np.array([list(i.ravel()) for i in test_y])

		_lowercase			:			Union[str, Any]										=						Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss="mean_squared_error", optimizer="adam")
		_lowercase			:			str										=						model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		_lowercase			:			Any										=						model.predict(x_test)









"""simple docstring"""






from __future__ import annotations






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   list[str] | None = None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =word_bank or []
			# create a table
			lowerCamelCase__		:		int		     =len(__lowerCamelCase     ) + 1

			lowerCamelCase__		:		list[list[list[str]]]		     =[]
			for _ in range(__lowerCamelCase     ):
						table.append([]     )
			# seed value
			lowerCamelCase__		:		Tuple		     =[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(__lowerCamelCase     ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(__lowerCamelCase     )] == word:
															lowerCamelCase__		:		list[list[str]]		     =[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(__lowerCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(__lowerCamelCase     )]:
						combination.reverse()

			return table[len(__lowerCamelCase     )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)




