






'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def 			SCREAMING_SNAKE_CASE_	()				->	Dict:
       lowerCamelCase__ :     Optional[int]	  =	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=UpperCamelCase    ,      default=1    ,      help="""Number of TPU cores to use (1 or 8)."""				)

       # positional
       parser.add_argument(
           """training_script"""    ,      type=UpperCamelCase    ,      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=UpperCamelCase				)

       return parser.parse_args()





def 			SCREAMING_SNAKE_CASE_	()				->	Dict:
       lowerCamelCase__ :     Any	  =	parse_args()

       # Import training_script as a module.
       lowerCamelCase__ :     Any	  =	Path(args.training_script				)
       sys.path.append(str(script_fpath.parent.resolve()				)				)
       lowerCamelCase__ :     Dict	  =	script_fpath.stem
       lowerCamelCase__ :     Tuple	  =	importlib.import_module(UpperCamelCase				)

       # Patch sys.argv
       lowerCamelCase__ :     Dict	  =	[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()









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

			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])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

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

