code: 
  
  
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  @contextmanager
  def     a						(      )     ->Optional[Any]:
  		def filter_log(_lowercase   :     Union[str, Any]       ):
  				return False if "This IS expected if you are initializing" in record.msg else True
  
  		UpperCamelCase__         =	datasets.utils.logging.get_logger("""transformers.modeling_utils"""       )
  		logger.addFilter(lowercase__       )
  		try:
  				yield
  		finally:
  				logger.removeFilter(lowercase__       )
  
  
  __lowerCAmelCase     =			'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'
  
  __lowerCAmelCase     =			'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'
  
  __lowerCAmelCase     =			'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,       _KWARGS_DESCRIPTION					)
  class   _UpperCAmelCase	(   datasets.Metric					):
  
  
  
  
  					def 	snake_case_		(						self       )						->  Tuple:
  							return datasets.MetricInfo(
  							    description=_DESCRIPTION    ,      citation=_CITATION    ,      homepage="""https://github.com/Tiiiger/bert_score"""    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
  							        {
  							            """predictions""": datasets.Value("""string"""    ,      id="""sequence"""       ),
  							            """references""": datasets.Sequence(datasets.Value("""string"""    ,      id="""sequence"""       )    ,      id="""references"""       ),
  							        }       )    ,      codebase_urls=["""https://github.com/Tiiiger/bert_score"""]    ,      reference_urls=[
  							        """https://github.com/Tiiiger/bert_score""",
  							        """https://arxiv.org/abs/1904.09675""",
  							    ]    ,      )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=64    ,      __lowerCAmelCase=4    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=False    ,      )						->  Tuple:
  							UpperCamelCase__         =	bert_score.utils.get_hash
  							UpperCamelCase__         =	bert_score.BERTScorer
  
  							if version.parse(bert_score.__version__       ) >= version.parse("""0.3.10"""       ):
  									UpperCamelCase__         =	functools.partial(A_    ,      use_fast_tokenizer=A_       )
  									UpperCamelCase__         =	functools.partial(A_    ,      use_fast_tokenizer=A_       )
  							elif use_fast_tokenizer:
  									raise ImportWarning(
  									    """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
  									    """You can install it with `pip install \"bert-score>=0.3.10\"`."""       )
  
  							if model_type is None:
  									assert lang is not None, "either lang or model_type should be specified"
  									UpperCamelCase__         =	bert_score.utils.langamodel[lang.lower()]
  
  							if num_layers is None:
  									UpperCamelCase__         =	bert_score.utils.modelalayers[model_type]
  
  							UpperCamelCase__         =	get_hash(
  							    model=A_    ,      num_layers=A_    ,      idf=A_    ,      rescale_with_baseline=A_    ,      use_custom_baseline=baseline_path is not None    ,      )
  
  							with filter_logging_context():
  									if not hasattr(self    ,      """cached_bertscorer"""       ) or self.cached_bertscorer.hash != hashcode:
  											UpperCamelCase__         =	scorer(
  											    model_type=A_    ,      num_layers=A_    ,      batch_size=A_    ,      nthreads=A_    ,      all_layers=A_    ,      idf=A_    ,      device=A_    ,      lang=A_    ,      rescale_with_baseline=A_    ,      baseline_path=A_    ,      )
  
  							((UpperCamelCase__)  ,			(UpperCamelCase__)  ,			(UpperCamelCase__))         =	self.cached_bertscorer.score(
  							    cands=A_    ,      refs=A_    ,      verbose=A_    ,      batch_size=A_    ,      )
  							UpperCamelCase__         =	{
  							    """precision""": P.tolist(),
  							    """recall""": R.tolist(),
  							    """f1""": F.tolist(),
  							    """hashcode""": hashcode,
  							}
  							return output_dict
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None    ,      **__lowerCAmelCase       )						->  str:
  							# References can be strings or lists of strings
  							# Let's change strings to lists of strings with one element
  							if references is not None:
  									UpperCamelCase__         =	[[ref] if isinstance(A_    ,      A_       ) else ref for ref in references]
  							super().add_batch(predictions=A_    ,      references=A_    ,      **A_       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=None    ,      **__lowerCAmelCase       )						->  Union[str, Any]:
  							# References can be strings or lists of strings
  							# Let's change strings to lists of strings with one element
  							if isinstance(A_    ,      A_       ):
  									UpperCamelCase__         =	[reference]
  							super().add(prediction=A_    ,      reference=A_    ,      **A_       )
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     List[str]       )     ->List[str]:
  		UpperCamelCase__         =	[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
  		for gap in gaps:
  				for i in range(_lowercase ,						len(_lowercase       )       ):
  						UpperCamelCase__         =	collection[i]
  						UpperCamelCase__         =	i
  						while j >= gap and collection[j - gap] > insert_value:
  								UpperCamelCase__         =	collection[j - gap]
  								j -= gap
  						if j != i:
  								UpperCamelCase__         =	insert_value
  		return collection
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:\n").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				print(shell_sort(unsorted))
  
  
  
  
  
  