




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 				_A  (		):
     """simple docstring"""

     raise RuntimeError("CUDA out of memory."     )





class 					__lowerCamelCase			(       nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	Any  ) ->			str:
         super().__init__()
         lowerCAmelCase__            =					nn.Linear(3  ,      4  )
         lowerCAmelCase__            =					nn.BatchNormad(4  )
         lowerCAmelCase__            =					nn.Linear(4  ,      5  )


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			int:
         return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE__  )  )  )






class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def   a (			self	:	Optional[Any]  ) ->			Tuple:
         lowerCAmelCase__            =					[]

         @find_executable_batch_size(starting_batch_size=128  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	Optional[int]  ):
              nonlocal batch_sizes
              batch_sizes.append(SCREAMING_SNAKE_CASE__  )
              if batch_size != 8:
                   raise_fake_out_of_memory()

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


    def   a (			self	:	Union[str, Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					[]

         @find_executable_batch_size(starting_batch_size=128  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	Any  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ):
              nonlocal batch_sizes
              batch_sizes.append(SCREAMING_SNAKE_CASE__  )
              if batch_size != 8:
                   raise_fake_out_of_memory()
              return batch_size, arga

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


    def   a (			self	:	Union[str, Any]  ) ->			Any:
         @find_executable_batch_size(starting_batch_size=0  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	List[Any]  ):
              pass

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


    def   a (			self	:	List[str]  ) ->			Any:
         @find_executable_batch_size(starting_batch_size=16  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	str  ):
              if batch_size > 0:
                   raise_fake_out_of_memory()
              pass

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


    def   a (			self	:	Dict  ) ->			Union[str, Any]:
         @find_executable_batch_size(starting_batch_size=128  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	Dict  ):
              if batch_size != 8:
                   raise raise_fake_out_of_memory()

         with self.assertRaises(SCREAMING_SNAKE_CASE__  ) 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	:	Optional[Any]  ) ->			List[Any]:
         @find_executable_batch_size(starting_batch_size=16  )
         def mock_training_loop_function(SCREAMING_SNAKE_CASE__	:	List[Any]  ):
              raise ValueError("Oops, we had an error!"  )

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


    @require_cuda
    def   a (			self	:	Optional[Any]  ) ->			List[str]:
         lowerCAmelCase__            =					torch.cuda.memory_allocated()
         lowerCAmelCase__            =					ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated()  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					release_memory(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(torch.cuda.memory_allocated()  ,      SCREAMING_SNAKE_CASE__  )




"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):

 if not isinstance(lowerCamelCase__       ,		lowerCamelCase__  ):
  raise TypeError("""Undefined for non-integers"""  )
 elif precision < 1:
  raise ValueError("""Undefined for non-natural numbers"""  )

 lowercase__       :       List[str]				=						precision
 lowercase__       :       Optional[int]				=						ceil(precision / 14  )
 lowercase__       :       Union[str, Any]				=						42_68_80 * Decimal(1_00_05  ).sqrt()
 lowercase__       :       Optional[Any]				=						1
 lowercase__       :       Optional[Any]				=						13_59_14_09
 lowercase__       :       List[Any]				=						Decimal(lowerCamelCase__  )
 for k in range(1       ,		lowerCamelCase__  ):
  lowercase__       :       Optional[int]				=						factorial(6 * k  ) // (factorial(3 * k  ) * factorial(lowerCamelCase__  ) ** 3)
  linear_term += 5_45_14_01_34
  exponential_term *= -26_25_37_41_26_40_76_80_00
  partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
 return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
   __snake_case    					=					50
   print(F"The first {n} digits of pi is: {pi(n)}")