

'''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'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



