


import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows

def 	lowerCAmelCase__			(      a__:		Any	)  ->		Optional[Any]:  # picklable for multiprocessing
							return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 	lowerCAmelCase__			(      )  ->		Any:
							with parallel_backend('spark'	):
														assert ParallelBackendConfig.backend_name == "spark"

							_UpperCAmelCase					=      [1, 2, 3]
							with pytest.raises(a__	):
														with parallel_backend('unsupported backend'	):
																					map_nested(a__      ,  a__      ,  num_proc=2	)

							with pytest.raises(a__	):
														with parallel_backend('unsupported backend'	):
																					map_nested(a__      ,  a__      ,  num_proc=-1	)







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'      ,  [2, -1]	)
def 	lowerCAmelCase__			(      a__:		List[str]	)  ->		Optional[Any]:
							_UpperCAmelCase					=      [1, 2]
							_UpperCAmelCase					=      {"a": 1, "b": 2}
							_UpperCAmelCase					=      {"a": [1, 2], "b": [3, 4]}
							_UpperCAmelCase					=      {"a": {"1": 1}, "b": 2}
							_UpperCAmelCase					=      {"a": 1, "b": 2, "c": 3, "d": 4}
							_UpperCAmelCase					=      [2, 3]
							_UpperCAmelCase					=      {"a": 2, "b": 3}
							_UpperCAmelCase					=      {"a": [2, 3], "b": [4, 5]}
							_UpperCAmelCase					=      {"a": {"1": 2}, "b": 3}
							_UpperCAmelCase					=      {"a": 2, "b": 3, "c": 4, "d": 5}

							with parallel_backend('spark'	):
														assert map_nested(a__      ,  a__      ,  num_proc=a__	) == expected_map_nested_sa
														assert map_nested(a__      ,  a__      ,  num_proc=a__	) == expected_map_nested_sa
														assert map_nested(a__      ,  a__      ,  num_proc=a__	) == expected_map_nested_sa
														assert map_nested(a__      ,  a__      ,  num_proc=a__	) == expected_map_nested_sa
														assert map_nested(a__      ,  a__      ,  num_proc=a__	) == expected_map_nested_sa




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__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :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])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
