





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def  lowerCAmelCase__(		)	->      Any:
  def filter_log(lowercase    :      Any			):
    return False if "This IS expected if you are initializing" in record.msg else True

  __snake_case				:			Dict            =    datasets.utils.logging.get_logger("transformers.modeling_utils"			)
  logger.addFilter(lowercase			)
  try:
    yield
  finally:
    logger.removeFilter(lowercase			)


_UpperCamelCase    =	'''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

_UpperCamelCase    =	'''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

_UpperCamelCase    =	'''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''




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



      """simple docstring"""






      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''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   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=False			,  UpperCAmelCase=None			,  UpperCAmelCase=64			,  UpperCAmelCase=4			,  UpperCAmelCase=False			,  UpperCAmelCase=False			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  )    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			int            =    bert_score.utils.get_hash
        __snake_case				:			Optional[int]            =    bert_score.BERTScorer

        if version.parse(bert_score.__version__				) >= version.parse("0.3.10"				):
          __snake_case				:			Dict            =    functools.partial(UpperCAmelCase			,  use_fast_tokenizer=UpperCAmelCase				)
          __snake_case				:			List[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				:			Union[str, Any]            =    bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
          __snake_case				:			Any            =    bert_score.utils.modelalayers[model_type]

        __snake_case				:			Optional[int]            =    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				:			List[Any]            =    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))				:			str            =    self.cached_bertscorer.score(
            cands=UpperCAmelCase			,  refs=UpperCAmelCase			,  verbose=UpperCAmelCase			,  batch_size=UpperCAmelCase			,  )
        __snake_case				:			int            =    {
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict







      def   UpperCAmelCase						(	self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase				)    ->				Dict:






        '''simple docstring'''






        if references is not None:
          __snake_case				:			int            =    [[ref] if isinstance(UpperCAmelCase			,  UpperCAmelCase				) else ref for ref in references]
        super().add_batch(predictions=UpperCAmelCase			,  references=UpperCAmelCase			,  **UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        if isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          __snake_case				:			List[Any]            =    [reference]
        super().add(prediction=UpperCAmelCase			,  reference=UpperCAmelCase			,  **UpperCAmelCase				)








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     _UpperCamelCase    =	list(input('''Type your message:\n'''))
     while True:
          try:
               _UpperCamelCase    =	int(input('''Please set token:(must be only digits)\n'''))
               break
          except Exception as error:
               print(error)
     for _ in range(token):
          rotator()
     for j in decode:
          engine(j)
     print('''\n''' + ''''''.join(code))
     print(
         F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
         '''this message again you should input same digits as token!'''
     )

