





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    Lock()






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0				,			10 ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(lowerCamelCase )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         a__						:    str   		=	rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         a__						:    Dict   		=	min(lowerCamelCase				,			lowerCamelCase )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(lowerCamelCase )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         a__						:    List[str]   		=	lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         a__						:    int   		=	max(lowerCamelCase				,			lowerCamelCase )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(lowerCamelCase )






def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    Optional[int]   		=	[]
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe() )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   a__						:    Optional[int]   		=	Pipe()
   a__						:    List[Any]   		=	Pipe()
   process_array_.append(
       Process(
           target=lowerCamelCase				,			args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])				,			) )
   a__						:    str   		=	temp_rs
   a__						:    str   		=	temp_rr

   for i in range(1				,			len(lowerCamelCase ) - 1 ):
      a__						:    Union[str, Any]   		=	Pipe()
      a__						:    Optional[int]   		=	Pipe()
      process_array_.append(
          Process(
              target=lowerCamelCase				,			args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])				,			) )
      a__						:    Tuple   		=	temp_rs
      a__						:    Dict   		=	temp_rr

   process_array_.append(
       Process(
           target=lowerCamelCase				,			args=(
               len(lowerCamelCase ) - 1,
               arr[len(lowerCamelCase ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(lowerCamelCase ) - 1],
           )				,			) )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0				,			len(lowerCamelCase ) ):
      a__						:    Optional[Any]   		=	result_pipe[p][0].recv()
      process_array_[p].join()
   return arr






def    _A	(   ):
   a__						:    Union[str, Any]   		=	list(range(10				,			0				,			-1 ) )
   print("Initial List" )
   print(*lowerCamelCase )
   a__						:    Dict   		=	odd_even_transposition(lowerCamelCase )
   print("Sorted List\n" )
   print(*lowerCamelCase )


if __name__ == "__main__":
  main()








from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

SCREAMING_SNAKE_CASE__							:      Tuple         =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION	)
class 	__lowerCAmelCase      (       datasets.Metric	):





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(self._get_feature_types()   )       ,      reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]       ,      )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"   )   ),
             "references": datasets.Sequence(datasets.Value("float"   )   ),
         }
      else:
         return {
             "predictions": datasets.Value("float"   ),
             "references": datasets.Value("float"   ),
         }





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

