


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

							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])
							__A	:	int     =     np.array(train_x)
							__A	:	int     =     np.array(test_x)
							__A	:	Union[str, Any]     =     np.array([list(i.ravel()) for i in train_y])
							__A	:	Dict     =     np.array([list(i.ravel()) for i in test_y])

							__A	:	str     =     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')
							__A	:	List[Any]     =     model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							__A	:	str     =     model.predict(x_test)




'''simple docstring'''






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


__A	:	Optional[int]     =     logging.get_logger(__name__)

__A	:	List[Any]     =     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'luke'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Tuple=5_0_2_6_7  ,_UpperCamelCase		:Tuple=5_0_0_0_0_0  ,_UpperCamelCase		:str=7_6_8  ,_UpperCamelCase		:List[Any]=2_5_6  ,_UpperCamelCase		:List[str]=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:Union[str, Any]=3_0_7_2  ,_UpperCamelCase		:int="gelu"  ,_UpperCamelCase		:int=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Dict=5_1_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:int=0.02  ,_UpperCamelCase		:Any=1E-1_2  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Any=1  ,_UpperCamelCase		:int=0  ,_UpperCamelCase		:List[Any]=2  ,**_UpperCamelCase		:str  ,):
									super().__init__(pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Any			       =						vocab_size
									snake_case_		:							List[Any]			       =						entity_vocab_size
									snake_case_		:							Optional[Any]			       =						hidden_size
									snake_case_		:							List[Any]			       =						entity_emb_size
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Union[str, Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						hidden_act
									snake_case_		:							Dict			       =						intermediate_size
									snake_case_		:							Any			       =						hidden_dropout_prob
									snake_case_		:							List[Any]			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[Any]			       =						max_position_embeddings
									snake_case_		:							Any			       =						type_vocab_size
									snake_case_		:							int			       =						initializer_range
									snake_case_		:							Union[str, Any]			       =						layer_norm_eps
									snake_case_		:							Tuple			       =						use_entity_aware_attention
									snake_case_		:							Tuple			       =						classifier_dropout