


"""simple docstring"""






from multiprocessing import Lock, Pipe, Process

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


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[Any]  ):
				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()
												SCREAMING_SNAKE_CASE__     							=				rr_cv[0].recv()
												process_lock.release()

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

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


def __lowercase     (      lowerCamelCase_  :      Optional[int]  ):
				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=lowerCamelCase_						,					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(lowerCamelCase_  ) - 1  ):
								SCREAMING_SNAKE_CASE__     							=				Pipe()
								SCREAMING_SNAKE_CASE__     							=				Pipe()
								process_array_.append(
								    Process(
								        target=lowerCamelCase_						,					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=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_  )  ):
								SCREAMING_SNAKE_CASE__     							=				result_pipe[p][0].recv()
								process_array_[p].join()
				return arr


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				list(range(10						,					0						,					-1  )  )
				print("Initial List"  )
				print(*lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				odd_even_transposition(lowerCamelCase_  )
				print("Sorted List\n"  )
				print(*lowerCamelCase_  )


if __name__ == "__main__":
	main()




"""simple docstring"""






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCamelCase     =  datasets.logging.get_logger(__name__)


_lowerCamelCase     =  '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_lowerCamelCase     =  '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_lowerCamelCase     =  '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_lowerCamelCase     =  {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


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





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,		citation=_CITATION       ,		homepage="https://github.com/google-research/bleurt"       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"       ,		id="sequence"     ),
								            "references": datasets.Value("string"       ,		id="sequence"     ),
								        }     )       ,		codebase_urls=["https://github.com/google-research/bleurt"]       ,		reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# check that config name specifies a valid BLEURT model
								if self.config_name == "default":
												logger.warning(
												    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
												    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.upper()

								else:
												raise KeyError(
												    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''     )

								# download the model checkpoint specified by self.config_name and set up the scorer
								SCREAMING_SNAKE_CASE__     							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
								SCREAMING_SNAKE_CASE__     							=				score.BleurtScorer(os.path.join(UpperCAmelCase__       ,		UpperCAmelCase__     )     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
