





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

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

					snake_case_   :				int		=     Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_   :				Optional[Any]		=     model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_   :				Any		=     model.predict(x_test)






'''simple docstring'''

import copy

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


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

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


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

