


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

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

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





'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''encoder-decoder'''
     UpperCAmelCase 					=				True






     def __init__(			self     ,							**__UpperCamelCase		)       ->    List[Any]:
          super().__init__(**__UpperCamelCase		)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          _a						=		kwargs.pop("encoder"		)
          _a						=		encoder_config.pop("model_type"		)
          _a						=		kwargs.pop("decoder"		)
          _a						=		decoder_config.pop("model_type"		)

          from ..auto.configuration_auto import AutoConfig

          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		True






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    PretrainedConfig:
          logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
          _a						=		True
          _a						=		True

          return cls(encoder=encoder_config.to_dict()     ,							decoder=decoder_config.to_dict()     ,							**__UpperCamelCase		)






     def  a_					(			self		)       ->    Any:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		self.encoder.to_dict()
          _a						=		self.decoder.to_dict()
          _a						=		self.__class__.model_type
          return output

