



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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  tf.constant([-100, -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 				_A			(	self       ):



				'''simple docstring'''


				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       )






from typing import Any

import numpy as np




def    UpperCAmelCase					(  lowercase__						:					np.ndarray	):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]

			# If matrix not symmetric, exit right away.
			if np.allclose(lowercase__				,					matrix.T	) is False:
						return False

			# Get eigenvalues and eignevectors for a symmetric matrix.
			a__				,       a__					=  np.linalg.eigh(lowercase__	)

			# Check sign of all eigenvalues.
			# np.all returns a value of type np.bool_
			return bool(np.all(eigen_values > 0	)	)




def    UpperCAmelCase					(  lowercase__						:					int	):



			'''simple docstring'''





			a__					=  np.random.randn(lowercase__				,					lowercase__	)
			a__					=  np.dot(lowercase__				,					random_matrix.T	)
			assert _is_matrix_spd(lowercase__	)
			return spd_matrix




def    UpperCAmelCase					(  lowercase__						:					np.ndarray				,					lowercase__						:					np.ndarray				,					lowercase__						:					int = 1000				,					lowercase__						:					float = 1e-8				,					):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]
			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[0]
			assert _is_matrix_spd(lowercase__	)

			# Initialize solution guess, residual, search direction.
			a__					=  np.zeros((np.shape(lowercase__	)[0], 1)	)
			a__					=  np.copy(lowercase__	)
			a__					=  np.copy(lowercase__	)

			# Set initial errors in solution guess and residual.
			a__					=  1e9
			a__					=  1e9
			a__					=  1e9

			# Set iteration counter to threshold number of iterations.
			a__					=  0

			while error > tol:
						# Save this value so we only calculate the matrix-vector product once.
						a__					=  np.dot(lowercase__				,					lowercase__	)

						# The main algorithm.

						# Update search direction magnitude.
						a__					=  np.dot(ra.T				,					lowercase__	) / np.dot(pa.T				,					lowercase__	)
						# Update solution guess.
						a__					=  xa + alpha * pa
						# Calculate new residual.
						a__					=  ra - alpha * w
						# Calculate new Krylov subspace scale.
						a__					=  np.dot(r.T				,					lowercase__	) / np.dot(ra.T				,					lowercase__	)
						# Calculate new A conjuage search direction.
						a__					=  r + beta * pa

						# Calculate errors.
						a__					=  np.linalg.norm(r - ra	)
						a__					=  np.linalg.norm(x - xa	)
						a__					=  np.maximum(lowercase__				,					lowercase__	)

						# Update variables.
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)

						# Update number of iterations.
						iterations += 1
						if iterations > max_iterations:
									break

			return x




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  3
			a__					=  _create_spd_matrix(lowercase__	)
			a__					=  np.random.randn(lowercase__				,					1	)
			a__					=  np.dot(lowercase__				,					lowercase__	)

			# Numpy solution.
			a__					=  np.linalg.solve(lowercase__				,					lowercase__	)

			# Our implementation.
			a__					=  conjugate_gradient(lowercase__				,					lowercase__	)

			# Ensure both solutions are close to x_true (and therefore one another).
			assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
			assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()

