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

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

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


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
