


'''simple docstring'''






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]     )
														lowerCAmelCase__      =			get_activation('''gelu'''     )
														self.assertTrue(torch.allclose(gelu_python(lowerCamelCase_     )      ,					torch_builtin(lowerCamelCase_     )     )     )
														self.assertFalse(torch.allclose(gelu_python(lowerCamelCase_     )      ,					gelu_new(lowerCamelCase_     )     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]     )
														lowerCAmelCase__      =			get_activation('''gelu'''     )
														lowerCAmelCase__      =			get_activation('''gelu_10'''     )

														lowerCAmelCase__      =			torch_builtin(lowerCamelCase_     )
														lowerCAmelCase__      =			geluaa(lowerCamelCase_     )

														lowerCAmelCase__      =			torch.where(y_gelu_aa < 10.0      ,					1      ,					0     )

														self.assertTrue(torch.max(lowerCamelCase_     ).item() == 10.0     )
														self.assertTrue(torch.allclose(y_gelu * clipped_mask      ,					y_gelu_aa * clipped_mask     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														get_activation('''gelu'''     )
														get_activation('''gelu_10'''     )
														get_activation('''gelu_fast'''     )
														get_activation('''gelu_new'''     )
														get_activation('''gelu_python'''     )
														get_activation('''gelu_pytorch_tanh'''     )
														get_activation('''linear'''     )
														get_activation('''mish'''     )
														get_activation('''quick_gelu'''     )
														get_activation('''relu'''     )
														get_activation('''sigmoid'''     )
														get_activation('''silu'''     )
														get_activation('''swish'''     )
														get_activation('''tanh'''     )
														with self.assertRaises(lowerCamelCase_     ):
																					get_activation('''bogus'''     )
														with self.assertRaises(lowerCamelCase_     ):
																					get_activation(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			get_activation('''gelu'''     )
														lowerCAmelCase__      =			1
														lowerCAmelCase__      =			get_activation('''gelu'''     )
														self.assertEqual(acta.a      ,					1     )
														with self.assertRaises(lowerCamelCase_     ):
																					lowerCAmelCase__      =			acta.a



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()