



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

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

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





from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


if __name__ == "__main__":
						_snake_case		=  mean_threshold(Image.open("path_to_image").convert("L"))
						image.save("output_image_path")
