




import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict





@pytest.mark.parametrize(
    """split_dict"""		,	[
        SplitDict(),
        SplitDict({"""train""": SplitInfo(name="""train"""		,	num_bytes=13_37		,	num_examples=42		,	dataset_name="""my_dataset"""				)}				),
        SplitDict({"""train""": SplitInfo(name="""train"""		,	num_bytes=13_37		,	num_examples=42				)}				),
        SplitDict({"""train""": SplitInfo()}				),
    ]		,	)
def 				A__ (snake_case   :		SplitDict				)     ->						Dict:
					__UpperCamelCase					:	int             =      split_dict._to_yaml_list()
					assert len(snake_case				) == len(snake_case				)
					__UpperCamelCase					:	Optional[Any]             =      SplitDict._from_yaml_list(snake_case				)
					for split_name, split_info in split_dict.items():
										# dataset_name field is deprecated, and is therefore not part of the YAML dump
										__UpperCamelCase					:	Any             =      None
										# the split name of split_dict takes over the name of the split info object
										__UpperCamelCase					:	str             =      split_name
					assert split_dict == reloaded







@pytest.mark.parametrize(
    """split_info"""		,	[SplitInfo(), SplitInfo(dataset_name=snake_case				), SplitInfo(dataset_name="""my_dataset"""				)]				)
def 				A__ (snake_case   :		int				)     ->						Optional[Any]:
					# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
					# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
					__UpperCamelCase					:	Any             =      asdict(SplitDict({"""train""": split_info}				)				)
					assert "dataset_name" in split_dict_asdict["train"]
					assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name












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

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

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






