'''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          =  1_0
						__lowercase          =  5
						__lowercase          =  2_0
						__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(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''')
						__lowercase          =  model.fit(
						    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
						)
						__lowercase          =  model.predict(x_test)





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




