

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

					_UpperCAmelCase						:  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""")
					_UpperCAmelCase						:  Optional[Any]									=			model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					_UpperCAmelCase						:  int									=			model.predict(x_test)







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




