


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

						snake_case							:	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''')
						snake_case							:	Tuple					      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						snake_case							:	Optional[int]					      =				model.predict(x_test)





from collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


if __name__ == "__main__":
						print(f"""{solution() = }""")




