import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def  __UpperCamelCase    (						):
					UpperCamelCase__										=      ArgumentParser(
					    description=(
					        '''PyTorch TPU distributed training launch '''
					        '''helper utility that will spawn up '''
					        '''multiple distributed processes'''
					    )							)

					# Optional arguments for the launch helper
					parser.add_argument('''--num_cores''' ,							type=A ,							default=1 ,							help='''Number of TPU cores to use (1 or 8).'''							)

					# positional
					parser.add_argument(
					    '''training_script''' ,							type=A ,							help=(
					        '''The full path to the single TPU training '''
					        '''program/script to be launched in parallel, '''
					        '''followed by all the arguments for the '''
					        '''training script'''
					    ) ,							)

					# rest from the training program
					parser.add_argument('''training_script_args''' ,							nargs=A							)

					return parser.parse_args()







def  __UpperCamelCase    (						):
					UpperCamelCase__										=      parse_args()

					# Import training_script as a module.
					UpperCamelCase__										=      Path(args.training_script							)
					sys.path.append(str(script_fpath.parent.resolve()							)							)
					UpperCamelCase__										=      script_fpath.stem
					UpperCamelCase__										=      importlib.import_module(A							)

					# Patch sys.argv
					UpperCamelCase__										=      [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores							)]
					xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
					main()




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

     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])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=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''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



