



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

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

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





import operator







def    _snake_case (       __snake_case					,  __snake_case = False					,  __snake_case = None    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		operator.lt if reverse else operator.gt
      UpperCAmelCase_  :	int				      =		solution or []

      if not arr:
            return solution

      UpperCAmelCase_  :	Union[str, Any]				      =		[arr.pop(0    )]
      for i, item in enumerate(__snake_case    ):
            if _operator(__snake_case					,  sublist[-1]    ):
                  sublist.append(__snake_case    )
                  arr.pop(__snake_case    )

    #  merging sublist into solution list
      if not solution:
            solution.extend(__snake_case    )
      else:
            while sublist:
                  UpperCAmelCase_  :	Optional[int]				      =		sublist.pop(0    )
                  for i, xx in enumerate(__snake_case    ):
                        if not _operator(__snake_case					,  __snake_case    ):
                              solution.insert(__snake_case					,  __snake_case    )
                              break
                  else:
                        solution.append(__snake_case    )

      strand_sort(__snake_case					,  __snake_case					,  __snake_case    )
      return solution


if __name__ == "__main__":
   assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
   assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
