




import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      get_activation("""swish"""	)

						self.assertIsInstance(lowerCAmelCase					,     nn.SiLU	)

						self.assertEqual(act(torch.tensor(-100					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertNotEqual(act(torch.tensor(-1					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(0					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(20					,     dtype=torch.floataa	)	).item()					,     20	)






	def 					lowerCamelCase__     (					self		:						int	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      get_activation("""silu"""	)

						self.assertIsInstance(lowerCAmelCase					,     nn.SiLU	)

						self.assertEqual(act(torch.tensor(-100					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertNotEqual(act(torch.tensor(-1					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(0					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(20					,     dtype=torch.floataa	)	).item()					,     20	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      get_activation("""mish"""	)

						self.assertIsInstance(lowerCAmelCase					,     nn.Mish	)

						self.assertEqual(act(torch.tensor(-200					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertNotEqual(act(torch.tensor(-1					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(0					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(20					,     dtype=torch.floataa	)	).item()					,     20	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      get_activation("""gelu"""	)

						self.assertIsInstance(lowerCAmelCase					,     nn.GELU	)

						self.assertEqual(act(torch.tensor(-100					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertNotEqual(act(torch.tensor(-1					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(0					,     dtype=torch.floataa	)	).item()					,     0	)
						self.assertEqual(act(torch.tensor(20					,     dtype=torch.floataa	)	).item()					,     20	)












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)






