



'''simple docstring'''

import os

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

import datasets


a							   =				datasets.logging.get_logger(__name__)


a							   =				'\\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'

a							   =				'\\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'

a							   =				'\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'

a							   =				{
    '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 						a_		(     datasets.Metric    ):


							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										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    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Optional[Any]			)			->					Tuple:
										# 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\').'			)
													snake_case:    Optional[Any]      							='bleurt-base-128'

										if self.config_name.lower() in CHECKPOINT_URLS:
													snake_case:    Union[str, Any]      							=self.config_name.lower()

										elif self.config_name.upper() in CHECKPOINT_URLS:
													snake_case:    List[Any]      							=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
										snake_case:    List[str]      							=dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]			)
										snake_case:    Optional[int]      							=score.BleurtScorer(os.path.join(a_      ,						a_			)			)




							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						List[Any]      ,						a_   :						Any			)			->					Any:
										snake_case:    Optional[Any]      							=self.scorer.score(references=a_      ,						candidates=a_			)
										return {"scores": scores}









'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


if __name__ == "__main__":
	a							   =				input('Enter numbers separated by a comma:\n').strip()
	a							   =				[int(item) for item in user_input.split(',')]
	print(patience_sort(unsorted))




