code: 
  '''simple docstring'''
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  
  
  @contextmanager
  def       _UpperCamelCase	(					)     ->       str:
  
  
  		'''simple docstring'''
  
  
  		def filter_log(SCREAMING_SNAKE_CASE__ ):
  				return False if "This IS expected if you are initializing" in record.msg else True
  
  		snake_case     :					int     =	datasets.utils.logging.get_logger('''transformers.modeling_utils''' )
  		logger.addFilter(SCREAMING_SNAKE_CASE__ )
  		try:
  				yield
  		finally:
  				logger.removeFilter(SCREAMING_SNAKE_CASE__ )
  
  
  lowercase__						=			"\\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"
  
  lowercase__						=			"\\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"
  
  lowercase__						=			"\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   snake_case__					(			datasets.Metric				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				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 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: List[Any]=None     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: str=False     ,  UpperCamelCase__							: Tuple=False     ,  UpperCamelCase__							: Dict=None     ,  UpperCamelCase__							: int=64     ,  UpperCamelCase__							: Optional[int]=4     ,  UpperCamelCase__							: str=False     ,  UpperCamelCase__							: Union[str, Any]=False     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: str=False     ,  )	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	bert_score.utils.get_hash
  				snake_case     :					List[Any]     =	bert_score.BERTScorer
  
  				if version.parse(bert_score.__version__			) >= version.parse('''0.3.10'''			):
  						snake_case     :					str     =	functools.partial(UpperCamelCase__     ,  use_fast_tokenizer=UpperCamelCase__			)
  						snake_case     :					str     =	functools.partial(UpperCamelCase__     ,  use_fast_tokenizer=UpperCamelCase__			)
  				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"
  						snake_case     :					Optional[Any]     =	bert_score.utils.langamodel[lang.lower()]
  
  				if num_layers is None:
  						snake_case     :					Tuple     =	bert_score.utils.modelalayers[model_type]
  
  				snake_case     :					str     =	get_hash(
  				    model=UpperCamelCase__     ,  num_layers=UpperCamelCase__     ,  idf=UpperCamelCase__     ,  rescale_with_baseline=UpperCamelCase__     ,  use_custom_baseline=baseline_path is not None     ,  )
  
  				with filter_logging_context():
  						if not hasattr(self     ,  '''cached_bertscorer'''			) or self.cached_bertscorer.hash != hashcode:
  								snake_case     :					Optional[int]     =	scorer(
  								    model_type=UpperCamelCase__     ,  num_layers=UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  nthreads=UpperCamelCase__     ,  all_layers=UpperCamelCase__     ,  idf=UpperCamelCase__     ,  device=UpperCamelCase__     ,  lang=UpperCamelCase__     ,  rescale_with_baseline=UpperCamelCase__     ,  baseline_path=UpperCamelCase__     ,  )
  
  				((snake_case) ,(snake_case) ,(snake_case))     :					Any     =	self.cached_bertscorer.score(
  				    cands=UpperCamelCase__     ,  refs=UpperCamelCase__     ,  verbose=UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  )
  				snake_case     :					List[str]     =	{
  				    '''precision''': P.tolist(),
  				    '''recall''': R.tolist(),
  				    '''f1''': F.tolist(),
  				    '''hashcode''': hashcode,
  				}
  				return output_dict
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: Dict=None     ,  UpperCamelCase__							: int=None     ,  **UpperCamelCase__							: int			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if references is not None:
  						snake_case     :					Optional[Any]     =	[[ref] if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) else ref for ref in references]
  				super().add_batch(predictions=UpperCamelCase__     ,  references=UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]     ,  UpperCamelCase__							: Tuple=None     ,  UpperCamelCase__							: List[Any]=None     ,  **UpperCamelCase__							: Any			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  						snake_case     :					Optional[int]     =	[reference]
  				super().add(prediction=UpperCamelCase__     ,  reference=UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
style_context: 
  '''simple docstring'''
  import itertools
  from pathlib import Path
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	[tuple(SCREAMING_SNAKE_CASE__ ) for login in logins]
  
  		snake_case     :					Dict     =	{char for login in split_logins for char in login}
  
  		for permutation in itertools.permutations(SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Optional[int]     =	True
  				for login in logins:
  						if not (
  						    permutation.index(login[0] )
  						    < permutation.index(login[1] )
  						    < permutation.index(login[2] )
  						):
  								snake_case     :					int     =	False
  								break
  
  				if satisfied:
  						return int(''''''.join(SCREAMING_SNAKE_CASE__ ) )
  
  		raise Exception('''Unable to find the secret passcode''' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "keylog.txt" )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	Path(SCREAMING_SNAKE_CASE__ ).parent.joinpath(SCREAMING_SNAKE_CASE__ ).read_text().splitlines()
  
  		return find_secret_passcode(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  