
"""simple docstring"""




import string

import numpy




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  return b if a == 0 else greatest_common_divisor(b % a ,				_UpperCamelCase						)


class 			__magic_name__       :
       UpperCamelCase_   =		string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       UpperCamelCase_   =		numpy.vectorize(lambda SCREAMING_SNAKE_CASE__ : x % 36 )

       UpperCamelCase_   =		numpy.vectorize(SCREAMING_SNAKE_CASE__ )
       def __init__(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.modulus(A_       )  # mod36 calc's on the encrypt key
         self.check_determinant()  # validate the determinant of the encryption key
         _lowercase:			List[Any]       =     encrypt_key.shape[0]
       def 							lowercase_							(     self ,				A_       )     ->	int:





         """simple docstring"""




         return self.key_string.index(A_       )
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         return self.key_string[round(A_       )]
       def 							lowercase_							(     self       )     ->	None:





         """simple docstring"""




         _lowercase:			int       =     round(numpy.linalg.det(self.encrypt_key       )       )

         if det < 0:
           _lowercase:			List[str]       =     det % len(self.key_string       )

         _lowercase:			List[str]       =     len(self.key_string       )
         if greatest_common_divisor(A_ ,				len(self.key_string       )       ) != 1:
           _lowercase:			Any       =     (
               f'''determinant modular {req_l} of encryption key({det}) '''
               f'''is not co prime w.r.t {req_l}.\nTry another key.'''
           )
           raise ValueError(A_       )
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Tuple       =     [char for char in text.upper() if char in self.key_string]

         _lowercase:			Optional[Any]       =     chars[-1]
         while len(A_       ) % self.break_key != 0:
           chars.append(A_       )

         return "".join(A_       )
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.process_text(text.upper()       )
         _lowercase:			Union[str, Any]       =     ''''''

         for i in range(0 ,				len(A_       ) - self.break_key + 1 ,				self.break_key       ):
           _lowercase:			Any       =     text[i : i + self.break_key]
           _lowercase:			int       =     [self.replace_letters(A_       ) for char in batch]
           _lowercase:			Optional[Any]       =     numpy.array([vec]       ).T
           _lowercase:			Optional[int]       =     self.modulus(self.encrypt_key.dot(A_       )       ).T.tolist()[
               0
           ]
           _lowercase:			Any       =     ''''''.join(
               self.replace_digits(A_       ) for num in batch_encrypted       )
           encrypted += encrypted_batch

         return encrypted
       def 							lowercase_							(     self       )     ->	numpy.ndarray:





         """simple docstring"""




         _lowercase:			Any       =     round(numpy.linalg.det(self.encrypt_key       )       )

         if det < 0:
           _lowercase:			Dict       =     det % len(self.key_string       )
         _lowercase:			Union[str, Any]       =     None
         for i in range(len(self.key_string       )       ):
           if (det * i) % len(self.key_string       ) == 1:
             _lowercase:			Union[str, Any]       =     i
             break

         _lowercase:			Union[str, Any]       =     (
             det_inv
             * numpy.linalg.det(self.encrypt_key       )
             * numpy.linalg.inv(self.encrypt_key       )
         )

         return self.to_int(self.modulus(A_       )       )



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     self.make_decrypt_key()
         _lowercase:			Union[str, Any]       =     self.process_text(text.upper()       )
         _lowercase:			Optional[int]       =     ''''''

         for i in range(0 ,				len(A_       ) - self.break_key + 1 ,				self.break_key       ):
           _lowercase:			int       =     text[i : i + self.break_key]
           _lowercase:			List[str]       =     [self.replace_letters(A_       ) for char in batch]
           _lowercase:			Tuple       =     numpy.array([vec]       ).T
           _lowercase:			int       =     self.modulus(decrypt_key.dot(A_       )       ).T.tolist()[0]
           _lowercase:			Any       =     ''''''.join(
               self.replace_digits(A_       ) for num in batch_decrypted       )
           decrypted += decrypted_batch

         return decrypted




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[Any]       =     int(input('''Enter the order of the encryption key: '''						)						)
  _lowercase:			List[Any]       =     []

  print('''Enter each row of the encryption key with space separated integers'''						)
  for _ in range(_UpperCamelCase						):
    _lowercase:			int       =     [int(_UpperCamelCase						) for x in input().split()]
    hill_matrix.append(_UpperCamelCase						)

  _lowercase:			Any       =     HillCipher(numpy.array(_UpperCamelCase						)						)

  print('''Would you like to encrypt or decrypt some text? (1 or 2)'''						)
  _lowercase:			List[Any]       =     input('''\n1. Encrypt\n2. Decrypt\n'''						)
  if option == "1":
    _lowercase:			str       =     input('''What text would you like to encrypt?: '''						)
    print('''Your encrypted text is:'''						)
    print(hc.encrypt(_UpperCamelCase						)						)
  elif option == "2":
    _lowercase:			Any       =     input('''What text would you like to decrypt?: '''						)
    print('''Your decrypted text is:'''						)
    print(hc.decrypt(_UpperCamelCase						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()


"""simple docstring"""




import comet  # From: unbabel-comet
import torch

import datasets


A__   :					int							=   datasets.logging.get_logger(__name__)

A__   :					Optional[Any]							=   '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

A__   :					Union[str, Any]							=   '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

A__   :					int							=   '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION )
class 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         return datasets.MetricInfo(
             description=_DESCRIPTION ,				citation=_CITATION ,				homepage='''https://unbabel.github.io/COMET/html/index.html''' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                 {
                     '''sources''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''predictions''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''references''': datasets.Value('''string''' ,				id='''sequence'''       ),
                 }       ) ,				codebase_urls=['''https://github.com/Unbabel/COMET'''] ,				reference_urls=[
                 '''https://github.com/Unbabel/COMET''',
                 '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
                 '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
             ] ,				)
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         if self.config_name == "default":
           _lowercase:			str       =     comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
         else:
           _lowercase:			Optional[Any]       =     comet.load_from_checkpoint(comet.download_model(self.config_name       )       )



       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_=None ,				A_=False       )     ->	List[str]:





         """simple docstring"""




         if gpus is None:
           _lowercase:			Union[str, Any]       =     1 if torch.cuda.is_available() else 0
         _lowercase:			Any       =     {'''src''': sources, '''mt''': predictions, '''ref''': references}
         _lowercase:			int       =     [dict(zip(A_ ,				A_       )       ) for t in zip(*data.values()       )]
         _lowercase ,				_lowercase:			Tuple       =     self.scorer.predict(A_ ,				gpus=A_ ,				progress_bar=A_       )
         return {"mean_score": mean_score, "scores": scores}
