

'''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 :      int					 =     pd.read_csv("sample_data.csv", header=None)
     lowerCamelCase :      Dict					 =     df.shape[:1][0]
     # If you're using some other dataset input the target column
     lowerCamelCase :      Dict					 =     df.iloc[:, 1:2]
     lowerCamelCase :      List[Any]					 =     actual_data.values.reshape(len_data, 1)
     lowerCamelCase :      Optional[Any]					 =     MinMaxScaler().fit_transform(actual_data)
     lowerCamelCase :      Tuple					 =     1_0
     lowerCamelCase :      int					 =     5
     lowerCamelCase :      int					 =     2_0
     lowerCamelCase :      List[str]					 =     len_data - periods * look_back
     lowerCamelCase :      Dict					 =     actual_data[:division]
     lowerCamelCase :      List[Any]					 =     actual_data[division - look_back :]
     lowerCamelCase ,    lowerCamelCase :      str					 =     [], []
     lowerCamelCase ,    lowerCamelCase :      Dict					 =     [], []

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

     lowerCamelCase :      Dict					 =     Sequential()
     model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
     model.add(Dense(forward_days))
     model.compile(loss="mean_squared_error", optimizer="adam")
     lowerCamelCase :      Tuple					 =     model.fit(
         x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
     )
     lowerCamelCase :      Union[str, Any]					 =     model.predict(x_test)



'''simple docstring'''
from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 							A__    (       A__     ,   A__  ):





  @register_to_config
  def __init__(		self      :	Dict							,					_a      :	int = 768							,					)	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.zeros(1							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.ones(1							,					_a ) )





  def 						A  (		self      :	Tuple							,					_a      :	Optional[Union[str, torch.device]] = None							,					_a      :	Optional[torch.dtype] = None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.mean.to(_a ).to(_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.std.to(_a ).to(_a ) )
       return self





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds - self.mean) * 1.0 / self.std
       return embeds





  def 						A  (		self      :	List[str]							,					_a      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds * self.std) + self.mean
       return embeds
