






'''simple docstring'''




def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       [0 for i in range(r + 1						)]
			# nc0 = 1
			snake_case_				:					Tuple   =       1
			for i in range(1     ,n + 1						):
						# to compute current row from previous row.
						snake_case_				:					Union[str, Any]   =       min(__magic_name__     ,__magic_name__						)
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]






print(binomial_coefficient(n=10, r=5))












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




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


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




