


'''simple docstring'''

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 				SCREAMING_SNAKE_CASE__(						)						->    Optional[Any]:





   '''simple docstring'''







   def filter_log(_UpperCamelCase					: Optional[int]      ):
      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(_UpperCamelCase      )
   try:
      yield
   finally:
      logger.removeFilter(_UpperCamelCase      )


__lowercase:						Tuple  		=   "\\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:						List[str]  		=   "\\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:						Tuple  		=   "\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 						lowercase_  (       self       :		Tuple    ):





    """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 						lowercase_  (       self       :		Optional[int],    a_       :		Optional[Any],    a_       :		Optional[int],    a_       :		Optional[int]=None,    a_       :		List[Any]=None,    a_       :		List[str]=None,    a_       :		Optional[int]=False,    a_       :		Dict=False,    a_       :		Union[str, Any]=None,    a_       :		str=64,    a_       :		List[Any]=4,    a_       :		Union[str, Any]=False,    a_       :		Optional[Any]=False,    a_       :		Any=None,    a_       :		Tuple=False,    ):





    """simple docstring"""



    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__)         =  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 						lowercase_  (       self       :		Optional[int],    a_       :		Optional[Any]=None,    a_       :		Union[str, Any]=None,    **a_       :		Any    ):





    """simple docstring"""



    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 						lowercase_  (       self       :		Tuple,    a_       :		List[Any]=None,    a_       :		Union[str, Any]=None,    **a_       :		Union[str, Any]    ):





    """simple docstring"""



    if isinstance(a_,    a_    ):
       UpperCamelCase__         =  [reference]
    super().add(prediction=a_,    reference=a_,    **a_    )





import random
import sys

from . import cryptomath_module as cryptomath

lowercase_		=							(
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->None:






					"""simple docstring"""






					if mode == "encrypt":
										if key_a == 1:
															sys.exit(
															    '''The affine cipher becomes weak when key '''
															    '''A is set to 1. Choose different key'''       )
										if key_b == 0:
															sys.exit(
															    '''The affine cipher becomes weak when key '''
															    '''B is set to 0. Choose different key'''       )
					if key_a < 0 or key_b < 0 or key_b > len(UpperCAmelCase       ) - 1:
										sys.exit(
										    '''Key A must be greater than 0 and key B must '''
										    F'''be between 0 and {len(UpperCAmelCase       ) - 1}.'''       )
					if cryptomath.gcd(UpperCAmelCase,	len(UpperCAmelCase       )       ) != 1:
										sys.exit(
										    F'''Key A {key_a} and the symbol set size {len(UpperCAmelCase       )} '''
										    '''are not relatively prime. Choose a different key.'''       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					__magic_name__   , __magic_name__					:							str		   =							divmod(UpperCAmelCase,	len(UpperCAmelCase       )       )
					check_keys(UpperCAmelCase,	UpperCAmelCase,	'''encrypt'''       )
					__magic_name__					:							Dict		   =							''''''
					for symbol in message:
										if symbol in SYMBOLS:
															__magic_name__					:							Union[str, Any]		   =							SYMBOLS.find(UpperCAmelCase       )
															cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(UpperCAmelCase       )]
										else:
															cipher_text += symbol
					return cipher_text







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					__magic_name__   , __magic_name__					:							List[str]		   =							divmod(UpperCAmelCase,	len(UpperCAmelCase       )       )
					check_keys(UpperCAmelCase,	UpperCAmelCase,	'''decrypt'''       )
					__magic_name__					:							Union[str, Any]		   =							''''''
					__magic_name__					:							int		   =							cryptomath.find_mod_inverse(UpperCAmelCase,	len(UpperCAmelCase       )       )
					for symbol in message:
										if symbol in SYMBOLS:
															__magic_name__					:							Optional[Any]		   =							SYMBOLS.find(UpperCAmelCase       )
															plain_text += SYMBOLS[
															    (sym_index - key_b) * mod_inverse_of_key_a % len(UpperCAmelCase       )
															]
										else:
															plain_text += symbol
					return plain_text







def  lowerCAmelCase  (	)  ->int:






					"""simple docstring"""






					while True:
										__magic_name__					:							List[Any]		   =							random.randint(2,	len(UpperCAmelCase       )       )
										__magic_name__					:							int		   =							random.randint(2,	len(UpperCAmelCase       )       )
										if cryptomath.gcd(UpperCAmelCase,	len(UpperCAmelCase       )       ) == 1 and key_b % len(UpperCAmelCase       ) != 0:
															return key_b * len(UpperCAmelCase       ) + key_b







def  lowerCAmelCase  (	)  ->None:






					"""simple docstring"""






					__magic_name__					:							List[Any]		   =							input('''Enter message: '''       ).strip()
					__magic_name__					:							Optional[int]		   =							int(input('''Enter key [2000 - 9000]: '''       ).strip()       )
					__magic_name__					:							List[Any]		   =							input('''Encrypt/Decrypt [E/D]: '''       ).strip().lower()

					if mode.startswith('''e'''       ):
										__magic_name__					:							Any		   =							'''encrypt'''
										__magic_name__					:							Any		   =							encrypt_message(UpperCAmelCase,	UpperCAmelCase       )
					elif mode.startswith('''d'''       ):
										__magic_name__					:							Optional[Any]		   =							'''decrypt'''
										__magic_name__					:							Union[str, Any]		   =							decrypt_message(UpperCAmelCase,	UpperCAmelCase       )
					print(F'''\n{mode.title()}ed text: \n{translated}'''       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# main()



