'''simple docstring'''





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Any			)							->      int:







  """simple docstring"""





  _validate_point(__UpperCamelCase			)
  _validate_point(__UpperCamelCase			)
  if len(__UpperCamelCase			) != len(__UpperCamelCase			):
    raise ValueError("""Both points must be in the same n-dimensional space"""			)

  return float(sum(abs(a - b			) for a, b in zip(__UpperCamelCase				,  __UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Optional[int]:







  """simple docstring"""





  if point:
    if isinstance(__UpperCamelCase				,  __UpperCamelCase			):
      for item in point:
        if not isinstance(__UpperCamelCase				,  (int, float)			):
          SCREAMING_SNAKE_CASE__    							=   (
              """Expected a list of numbers as input, found """
              f"""{type(__UpperCamelCase			).__name__}"""
          )
          raise TypeError(__UpperCamelCase			)
    else:
      SCREAMING_SNAKE_CASE__    							=   f"""Expected a list of numbers as input, found {type(__UpperCamelCase			).__name__}"""
      raise TypeError(__UpperCamelCase			)
  else:
    raise ValueError("""Missing an input"""			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Optional[int]			)							->      int:







  """simple docstring"""





  _validate_point(__UpperCamelCase			)
  _validate_point(__UpperCamelCase			)
  if len(__UpperCamelCase			) != len(__UpperCamelCase			):
    raise ValueError("""Both points must be in the same n-dimensional space"""			)

  return float(sum(abs(x - y			) for x, y in zip(__UpperCamelCase				,  __UpperCamelCase			)			)			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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 __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
