

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

							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])
							_SCREAMING_SNAKE_CASE					:  List[Any]             =      np.array(train_x)
							_SCREAMING_SNAKE_CASE					:  str             =      np.array(test_x)
							_SCREAMING_SNAKE_CASE					:  Dict             =      np.array([list(i.ravel()) for i in train_y])
							_SCREAMING_SNAKE_CASE					:  int             =      np.array([list(i.ravel()) for i in test_y])

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





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


