
"""simple docstring"""

import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def 		UpperCAmelCase (		):





  '''simple docstring'''


  raise RuntimeError('CUDA out of memory.'      )






class 	_lowercase       (    nn.Module		):




 def __init__(			self )		->							Tuple:
   super().__init__()
   lowerCamelCase					:      Optional[int]          =   nn.Linear(3		,			4 )
   lowerCamelCase					:      List[str]          =   nn.BatchNormad(4 )
   lowerCamelCase					:      List[Any]          =   nn.Linear(4		,			5 )




 def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
   return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase_ ) ) )






class 	_lowercase       (    unittest.TestCase		):




 def       _UpperCamelCase   (			self )		->							Union[str, Any]:
   lowerCamelCase					:      int          =   []

   @find_executable_batch_size(starting_batch_size=128 )
   def mock_training_loop_function(UpperCAmelCase_ ):
     nonlocal batch_sizes
     batch_sizes.append(UpperCAmelCase_ )
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(UpperCAmelCase_		,			[128, 64, 32, 16, 8] )




 def       _UpperCamelCase   (			self )		->							Union[str, Any]:
   lowerCamelCase					:      List[str]          =   []

   @find_executable_batch_size(starting_batch_size=128 )
   def mock_training_loop_function(UpperCAmelCase_		,			UpperCAmelCase_ ):
     nonlocal batch_sizes
     batch_sizes.append(UpperCAmelCase_ )
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowerCamelCase					:      int          =   mock_training_loop_function('hello' )
   self.assertListEqual(UpperCAmelCase_		,			[128, 64, 32, 16, 8] )
   self.assertListEqual([bs, arga]		,			[8, 'hello'] )




 def       _UpperCamelCase   (			self )		->							str:
   @find_executable_batch_size(starting_batch_size=0 )
   def mock_training_loop_function(UpperCAmelCase_ ):
     pass

   with self.assertRaises(UpperCAmelCase_ ) as cm:
     mock_training_loop_function()
     self.assertIn('No executable batch size found, reached zero.'		,			cm.exception.args[0] )




 def       _UpperCamelCase   (			self )		->							Optional[Any]:
   @find_executable_batch_size(starting_batch_size=16 )
   def mock_training_loop_function(UpperCAmelCase_ ):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(UpperCAmelCase_ ) as cm:
     mock_training_loop_function()
     self.assertIn('No executable batch size found, reached zero.'		,			cm.exception.args[0] )




 def       _UpperCamelCase   (			self )		->							Optional[int]:
   @find_executable_batch_size(starting_batch_size=128 )
   def mock_training_loop_function(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ ):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(UpperCAmelCase_ ) as cm:
     mock_training_loop_function(128		,			'hello'		,			'world' )
     self.assertIn('Batch size was passed into `f`'		,			cm.exception.args[0] )
     self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'		,			cm.exception.args[0] )




 def       _UpperCamelCase   (			self )		->							List[str]:
   @find_executable_batch_size(starting_batch_size=16 )
   def mock_training_loop_function(UpperCAmelCase_ ):
     raise ValueError('Oops, we had an error!' )

   with self.assertRaises(UpperCAmelCase_ ) as cm:
     mock_training_loop_function()
     self.assertIn('Oops, we had an error!'		,			cm.exception.args[0] )




 @require_cuda
 def       _UpperCamelCase   (			self )		->							Dict:
   lowerCamelCase					:      Any          =   torch.cuda.memory_allocated()
   lowerCamelCase					:      Optional[int]          =   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()		,			UpperCAmelCase_ )
   lowerCamelCase					:      int          =   release_memory(UpperCAmelCase_ )
   self.assertEqual(torch.cuda.memory_allocated()		,			UpperCAmelCase_ )








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase (		a_,						a_,						a_ = 1E-12,						a_ = 100,						):





		'''simple docstring'''


		assert np.shape(a_      )[0] == np.shape(a_      )[1]
		# Ensure proper dimensionality.
		assert np.shape(a_      )[0] == np.shape(a_      )[0]
		# Ensure inputs are either both complex or both real
		assert np.iscomplexobj(a_      ) == np.iscomplexobj(a_      )
		lowerCamelCase					:      Optional[int]          =   np.iscomplexobj(a_      )
		if is_complex:
				# Ensure complex input_matrix is Hermitian
				assert np.array_equal(a_,						input_matrix.conj().T      )

		# Set convergence to False. Will define convergence when we exceed max_iterations
		# or when we have small changes from one iteration to next.

		lowerCamelCase					:      Union[str, Any]          =   False
		lowerCamelCase					:      List[str]          =   0
		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      Dict          =   1E12

		while not convergence:
				# Multiple matrix by the vector.
				lowerCamelCase					:      Optional[int]          =   np.dot(a_,						a_      )
				# Normalize the resulting output vector.
				lowerCamelCase					:      Optional[int]          =   w / np.linalg.norm(a_      )
				# Find rayleigh quotient
				# (faster than usual b/c we know vector is normalized already)
				lowerCamelCase					:      Optional[Any]          =   vector.conj().T if is_complex else vector.T
				lowerCamelCase					:      str          =   np.dot(a_,						np.dot(a_,						a_      )      )

				# Check convergence.
				lowerCamelCase					:      Optional[int]          =   np.abs(lambda_ - lambda_previous      ) / lambda_
				iterations += 1

				if error <= error_tol or iterations >= max_iterations:
						lowerCamelCase					:      int          =   True

				lowerCamelCase					:      Optional[Any]          =   lambda_

		if is_complex:
				lowerCamelCase					:      Any          =   np.real(lambda_      )

		return lambda_, vector
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		lowerCamelCase					:      str          =   np.array([41, 4, 20]      )
		lowerCamelCase					:      Optional[Any]          =   real_input_matrix.astype(np.complexaaa      )
		lowerCamelCase					:      Dict          =   np.triu(1j * complex_input_matrix,						1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		lowerCamelCase					:      List[Any]          =   np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						lowerCamelCase					:      str          =   real_input_matrix
						lowerCamelCase					:      Any          =   real_vector
				elif problem_type == "complex":
						lowerCamelCase					:      str          =   complex_input_matrix
						lowerCamelCase					:      Dict          =   complex_vector

				# Our implementation.
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   power_iteration(a_,						a_      )

				# Numpy implementation.

				# Get eigenvalues and eigenvectors using built-in numpy
				# eigh (eigh used for symmetric or hermetian matrices).
				lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   np.linalg.eigh(a_      )
				# Last eigenvalue is the maximum one.
				lowerCamelCase					:      Dict          =   eigen_values[-1]
				# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
				lowerCamelCase					:      List[str]          =   eigen_vectors[:, -1]

				# Check our implementation and numpy gives close answers.
				assert np.abs(eigen_value - eigen_value_max      ) <= 1E-6
				# Take absolute values element wise of each eigenvector.
				# as they are only unique to a minus sign.
				assert np.linalg.norm(np.abs(a_      ) - np.abs(a_      )      ) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()






