





"""simple docstring"""


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)







"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__				  =   (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__				  =   [ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__				  =   {ord(char) for char in VALID_CHARS}

lowerCamelCase__				  =   ["the", "be", "to", "of", "and", "in", "that", "have"]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str | None:




      """simple docstring"""

      _UpperCamelCase      :	str						=       ""
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int

      for keychar, cipherchar in zip(cycle(lowercase_			)		,lowercase_			):
            _UpperCamelCase      :	Dict						=       cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(lowercase_			)

      return decoded




def 					lowercase__    (					lowercase_			)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	list[str]						=       []
      for key in product(lowercase_		,repeat=3			):
            _UpperCamelCase      :	int						=       try_key(lowercase_		,lowercase_			)
            if encoded is not None:
                  possibles.append(lowercase_			)
      return possibles




def 					lowercase__    (					lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

      return [possible for possible in possibles if common_word in possible.lower()]




def 					lowercase__    (					lowercase_ = "p059_cipher.txt"			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	list[int]
      _UpperCamelCase      :	list[str]
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str						=       Path(lowercase_			).parent.joinpath(lowercase_			).read_text(encoding="utf-8"			)

      _UpperCamelCase      :	Optional[Any]						=       [int(lowercase_			) for number in data.strip().split(","			)]

      _UpperCamelCase      :	List[str]						=       filter_valid_chars(lowercase_			)
      for common_word in COMMON_WORDS:
            _UpperCamelCase      :	Union[str, Any]						=       filter_common_word(lowercase_		,lowercase_			)
            if len(lowercase_			) == 1:
                  break

      _UpperCamelCase      :	Union[str, Any]						=       possibles[0]
      return sum(ord(lowercase_			) for char in decoded_text			)


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