






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

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













from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   pyspark.sql.DataFrame ,							SCREAMING_SNAKE_CASE_     :   Optional[NamedSplit] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[Features] = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   str = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   str = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   str = "arrow" ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											super().__init__(
											    split=SCREAMING_SNAKE_CASE_ ,							features=SCREAMING_SNAKE_CASE_ ,							cache_dir=SCREAMING_SNAKE_CASE_ ,							keep_in_memory=SCREAMING_SNAKE_CASE_ ,							streaming=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						load_from_cache_file
											lowerCAmelCase__      			=						file_format
											lowerCAmelCase__      			=						Spark(
											    df=SCREAMING_SNAKE_CASE_ ,							features=SCREAMING_SNAKE_CASE_ ,							cache_dir=SCREAMING_SNAKE_CASE_ ,							working_dir=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)




							def 						__snake_case					(   self     :   List[str]	):
											if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split	)
											lowerCAmelCase__      			=						None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=SCREAMING_SNAKE_CASE_ ,							file_format=self._file_format ,							)
											return self.builder.as_dataset(split=self.split	)





