


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 	__lowerCAmelCase   ()->  Union[str, Any]:




      """simple docstring"""

      raise RuntimeError('''CUDA out of memory.''' )







class lowerCAmelCase_						(			nn.Module	):

 '''simple docstring'''

 def __init__(     self ):
       super().__init__()
       snake_case_	=				nn.Linear(3		,				4 )
       snake_case_	=				nn.BatchNormad(4 )
       snake_case_	=				nn.Linear(4		,				5 )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_ ) ) )







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				[]

       @find_executable_batch_size(starting_batch_size=1_28 )
       def mock_training_loop_function(_UpperCAmelCase ):
             nonlocal batch_sizes
             batch_sizes.append(lowerCamelCase_ )
             if batch_size != 8:
                   raise_fake_out_of_memory()

       mock_training_loop_function()
       self.assertListEqual(lowerCamelCase_		,				[1_28, 64, 32, 16, 8] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				[]

       @find_executable_batch_size(starting_batch_size=1_28 )
       def mock_training_loop_function(_UpperCAmelCase		,				_UpperCAmelCase ):
             nonlocal batch_sizes
             batch_sizes.append(lowerCamelCase_ )
             if batch_size != 8:
                   raise_fake_out_of_memory()
             return batch_size, arga

       snake_case_	,   snake_case_	=				mock_training_loop_function('''hello''' )
       self.assertListEqual(lowerCamelCase_		,				[1_28, 64, 32, 16, 8] )
       self.assertListEqual([bs, arga]		,				[8, '''hello'''] )



 def 						UpperCamelCase__					(     self ):
       @find_executable_batch_size(starting_batch_size=0 )
       def mock_training_loop_function(_UpperCAmelCase ):
             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 						UpperCamelCase__					(     self ):
       @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(lowerCamelCase_ ) as cm:
             mock_training_loop_function()
             self.assertIn('''No executable batch size found, reached zero.'''		,				cm.exception.args[0] )



 def 						UpperCamelCase__					(     self ):
       @find_executable_batch_size(starting_batch_size=1_28 )
       def mock_training_loop_function(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
             if batch_size != 8:
                   raise raise_fake_out_of_memory()

       with self.assertRaises(lowerCamelCase_ ) as cm:
             mock_training_loop_function(1_28		,				'''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 ):
       @find_executable_batch_size(starting_batch_size=16 )
       def mock_training_loop_function(_UpperCAmelCase ):
             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 						UpperCamelCase__					(     self ):
       snake_case_	=				torch.cuda.memory_allocated()
       snake_case_	=				ModelForTest()
       model.cuda()
       self.assertGreater(torch.cuda.memory_allocated()		,				lowerCamelCase_ )
       snake_case_	=				release_memory(lowerCamelCase_ )
       self.assertEqual(torch.cuda.memory_allocated()		,				lowerCamelCase_ )



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
            snake_case_	=				f'''Input value of [number={number}] must be an integer'''
            raise TypeError(SCREAMING_SNAKE_CASE )

      if number < 1:
            snake_case_	=				f'''Input value of [number={number}] must be > 0'''
            raise ValueError(SCREAMING_SNAKE_CASE )

      snake_case_	=				1

      for i in range(1 , SCREAMING_SNAKE_CASE ):
            current_number *= 4 * i - 2
            current_number //= i + 1

      return current_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()