


'''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 		_lowercase ():




     '''simple docstring'''





     raise RuntimeError("CUDA out of memory."							)







class 							__magic_name__	(       nn.Module ):

     """simple docstring"""






     def __init__(   self		):


          '''simple docstring'''





          super().__init__()
          __A	:       Optional[int]      =							nn.Linear(3			,     4		)
          __A	:       int      =							nn.BatchNormad(4		)
          __A	:       Dict      =							nn.Linear(4			,     5		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          return self.lineara(self.batchnorm(self.lineara(lowerCamelCase		)		)		)







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							[]

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

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							[]

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

          __A  ,__A	:       Tuple      =							mock_training_loop_function("hello"		)
          self.assertListEqual(lowerCamelCase			,     [128, 64, 32, 16, 8]		)
          self.assertListEqual([bs, arga]			,     [8, "hello"]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          @find_executable_batch_size(starting_batch_size=0		)
          def mock_training_loop_function(lowerCamelCase		):
               pass

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          @find_executable_batch_size(starting_batch_size=16		)
          def mock_training_loop_function(lowerCamelCase		):
               if batch_size > 0:
                    raise_fake_out_of_memory()
               pass

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          @find_executable_batch_size(starting_batch_size=128		)
          def mock_training_loop_function(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):
               if batch_size != 8:
                    raise raise_fake_out_of_memory()

          with self.assertRaises(lowerCamelCase		) 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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          @find_executable_batch_size(starting_batch_size=16		)
          def mock_training_loop_function(lowerCamelCase		):
               raise ValueError("Oops, we had an error!"		)

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


     @require_cuda
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							torch.cuda.memory_allocated()
          __A	:       str      =							ModelForTest()
          model.cuda()
          self.assertGreater(torch.cuda.memory_allocated()			,     lowerCamelCase		)
          __A	:       Tuple      =							release_memory(lowerCamelCase		)
          self.assertEqual(torch.cuda.memory_allocated()			,     lowerCamelCase		)





'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

