

"""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					(    )       ->				Optional[Any]:
      raise RuntimeError("""CUDA out of memory."""							)

class      __a							(nn.Module):






     '''simple docstring'''

     def __init__(						self  )  ->      str:


           """simple docstring"""


           super().__init__()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	nn.Linear(3	,    4  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	nn.BatchNormad(4  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	nn.Linear(4	,    5  )





     def 					_a			(						self	,    _a  )  ->      Any:


           """simple docstring"""


           return self.lineara(self.batchnorm(self.lineara(_a  )  )  )





class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	[]

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

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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[]

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

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	mock_training_loop_function("""hello"""  )
           self.assertListEqual(_a	,    [128, 64, 32, 16, 8]  )
           self.assertListEqual([bs, arga]	,    [8, """hello"""]  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


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

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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


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

           with self.assertRaises(_a  ) 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 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


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

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





     @require_cuda
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.cuda.memory_allocated()
           SCREAMING_SNAKE_CASE__   :      int      		=	ModelForTest()
           model.cuda()
           self.assertGreater(torch.cuda.memory_allocated()	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	release_memory(_a  )
           self.assertEqual(torch.cuda.memory_allocated()	,    _a  )







"""simple docstring"""


from math import sqrt




def  _lowercase					(    __lowerCAmelCase							)       ->				bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5  ,					int(sqrt(__lowerCAmelCase							) + 1							)  ,					6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def  _lowercase					(    __lowerCAmelCase = 1_0001							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	1
      while count != nth and number < 3:
            number += 1
            if is_prime(__lowerCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__lowerCAmelCase							):
                  count += 1
      return number


if __name__ == "__main__":
  print(f'{solution() = }')




