
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
lowercase__											=Lock()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__						):
	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()
			a_      				=						rr_cv[0].recv()
			process_lock.release()

			# take the lower value since you are on the left
			a_      				=						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()
			a_      				=						lr_cv[0].recv()
			process_lock.release()

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






def      UpperCamelCase_				(      A__						):
	a_      				=						[]
	a_      				=						[]
	# 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_      				=						Pipe()
	a_      				=						Pipe()
	process_array_.append(
	    Process(
	        target=A__  ,      args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])  ,      )						)
	a_      				=						temp_rs
	a_      				=						temp_rr

	for i in range(1  ,      len(A__						) - 1						):
		a_      				=						Pipe()
		a_      				=						Pipe()
		process_array_.append(
		    Process(
		        target=A__  ,      args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])  ,      )						)
		a_      				=						temp_rs
		a_      				=						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__						)						):
		a_      				=						result_pipe[p][0].recv()
		process_array_[p].join()
	return arr






def      UpperCamelCase_				(      ):
	a_      				=						list(range(10  ,      0  ,      -1						)						)
	print("""Initial List"""						)
	print(*A__						)
	a_      				=						odd_even_transposition(A__						)
	print("""Sorted List\n"""						)
	print(*A__						)


if __name__ == "__main__":
		main()


'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


lowercase__											=datasets.logging.get_logger(__name__)

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

lowercase__											='\\nCrosslingual 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).\nWith 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.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

lowercase__											='\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'







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




			def 					lowerCAmelCase__					(       self	):
				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 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if self.config_name == "default":
					a_      				=						comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""	)	)
				else:
					a_      				=						comet.load_from_checkpoint(comet.download_model(self.config_name	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase=False	):
				if gpus is None:
					a_      				=						1 if torch.cuda.is_available() else 0
				a_      				=						{"""src""": sources, """mt""": predictions, """ref""": references}
				a_      				=						[dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	) for t in zip(*data.values()	)]
				a_				,  a_      				=						self.scorer.predict(UpperCAmelCase					,   gpus=UpperCAmelCase					,   progress_bar=UpperCAmelCase	)
				return {"mean_score": mean_score, "scores": scores}
