

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					:  List[str]             =      Lock()
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     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(_A    )
               process_lock.release()

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

               # take the lower value since you are on the left
               SCREAMING_SNAKE_CASE__   =   min(_A				,		_A    )
          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(_A    )
               process_lock.release()

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

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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   []
     # 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
     SCREAMING_SNAKE_CASE__   =   Pipe()
     SCREAMING_SNAKE_CASE__   =   Pipe()
     process_array_.append(
         Process(
             target=_A				,		args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])				,		)    )
     SCREAMING_SNAKE_CASE__   =   temp_rs
     SCREAMING_SNAKE_CASE__   =   temp_rr

     for i in range(1				,		len(_A    ) - 1    ):
          SCREAMING_SNAKE_CASE__   =   Pipe()
          SCREAMING_SNAKE_CASE__   =   Pipe()
          process_array_.append(
              Process(
                  target=_A				,		args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])				,		)    )
          SCREAMING_SNAKE_CASE__   =   temp_rs
          SCREAMING_SNAKE_CASE__   =   temp_rr

     process_array_.append(
         Process(
             target=_A				,		args=(
                 len(_A    ) - 1,
                 arr[len(_A    ) - 1],
                 temp_ls,
                 None,
                 temp_lr,
                 None,
                 result_pipe[len(_A    ) - 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(_A    )    ):
          SCREAMING_SNAKE_CASE__   =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   list(range(10				,		0				,		-1    )    )
     print('''Initial List'''    )
     print(*_A    )
     SCREAMING_SNAKE_CASE__   =   odd_even_transposition(_A    )
     print('''Sorted List\n'''    )
     print(*_A    )


if __name__ == "__main__":
       main()





import comet  # From: unbabel-comet
import torch

import datasets


_SCREAMING_SNAKE_CASE					:  List[str]             =      datasets.logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Any             =      '''\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = "{COMET}: A Neural Framework for {MT} Evaluation",
   author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
   month = nov,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
   pages = "2685--2702",
}
'''

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''






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






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://unbabel.github.io/COMET/html/index.html'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''sources''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''predictions''': datasets.Value('''string'''     ,       id='''sequence''' ),
                   '''references''': datasets.Value('''string'''     ,       id='''sequence''' ),
               } )     ,       codebase_urls=['''https://github.com/Unbabel/COMET''']     ,       reference_urls=[
               '''https://github.com/Unbabel/COMET''',
               '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
               '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
           ]     ,       )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Tuple:
       if self.config_name == "default":
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
       else:
            SCREAMING_SNAKE_CASE__   =   comet.load_from_checkpoint(comet.download_model(self.config_name ) )







  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict=None     ,       __lowerCamelCase						:		Optional[int]=False )		->							str:
       if gpus is None:
            SCREAMING_SNAKE_CASE__   =   1 if torch.cuda.is_available() else 0
       SCREAMING_SNAKE_CASE__   =   {'''src''': sources, '''mt''': predictions, '''ref''': references}
       SCREAMING_SNAKE_CASE__   =   [dict(zip(__lowerCamelCase     ,       __lowerCamelCase ) ) for t in zip(*data.values() )]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.scorer.predict(__lowerCamelCase     ,       gpus=__lowerCamelCase     ,       progress_bar=__lowerCamelCase )
       return {"mean_score": mean_score, "scores": scores}


