




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

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





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
   import accelerate

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			int:
      if not is_accelerate_available():
            return method
      SCREAMING_SNAKE_CASE_     						=      version.parse(accelerate.__version__							).base_version
      if version.parse(__UpperCAmelCase							) < version.parse('0.17.0'							):
            return method

      def wrapper(self						:     Optional[int]      ,      *__UpperCAmelCase						:     Optional[Any]      ,      **__UpperCAmelCase						:     Optional[Any]							):
            if hasattr(self      ,      '_hf_hook'							) and hasattr(self._hf_hook      ,      'pre_forward'							):
                  self._hf_hook.pre_forward(self							)
            return method(self      ,      *__UpperCAmelCase      ,      **__UpperCAmelCase							)

      return wrapper