


'''simple docstring'''
import numpy as np







def 			a_							(  UpperCamelCase_   ):
							A_						,				A_ 						=		a.shape
							A_ 						=		min(UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		np.eye(UpperCamelCase_   )
							A_ 						=		a.copy()

							for k in range(t - 1   ):
														# select a column of modified matrix A':
														A_ 						=		r[k:, [k]]
														# construct first basis vector
														A_ 						=		np.zeros_like(UpperCamelCase_   )
														A_ 						=		1.0
														# determine scaling factor
														A_ 						=		np.linalg.norm(UpperCamelCase_   )
														# construct vector v for Householder reflection
														A_ 						=		x + np.sign(x[0]   ) * alpha * ea
														v /= np.linalg.norm(UpperCamelCase_   )

														# construct the Householder matrix
														A_ 						=		np.eye(m - k   ) - 2.0 * v @ v.T
														# pad with ones and zeros as necessary
														A_ 						=		np.block([[np.eye(UpperCamelCase_   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

														A_ 						=		q @ q_k.T
														A_ 						=		q_k @ r

							return q, r


if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''simple docstring'''
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers.activations_tf import get_tf_activation






@require_tf
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		tf.where(y_gelu_aa < 1_0.0   ,     1.0   ,     0.0  )

													self.assertEqual(tf.math.reduce_max(lowerCAmelCase  ).numpy().item()   ,     1_0.0  )
													self.assertTrue(np.allclose(y_gelu * clipped_mask   ,     y_gelu_aa * clipped_mask  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													get_tf_activation("gelu"  )
													get_tf_activation("gelu_10"  )
													get_tf_activation("gelu_fast"  )
													get_tf_activation("gelu_new"  )
													get_tf_activation("glu"  )
													get_tf_activation("mish"  )
													get_tf_activation("quick_gelu"  )
													get_tf_activation("relu"  )
													get_tf_activation("sigmoid"  )
													get_tf_activation("silu"  )
													get_tf_activation("swish"  )
													get_tf_activation("tanh"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation("bogus"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation(lowerCAmelCase  )





