


import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


snake_case				:    Tuple       				=     '''\
    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
'''

snake_case				:    int       				=     '''\
    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
'''

snake_case				:    Tuple       				=     '''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "precision": Precision@10
Examples:

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'precision@10\': 1.0}

'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      return float((preds == labels).mean()						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						:Tuple 							=       simple_accuracy(UpperCAmelCase_ ,  UpperCAmelCase_						)
      a						:Optional[int] 							=       float(fa_score(y_true=UpperCAmelCase_ ,  y_pred=UpperCAmelCase_						)						)
      return {
          "accuracy": acc,
          "f1": fa,
      }
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:Tuple 							=       np.array(UpperCAmelCase_						)
      a						:Dict 							=       np.array(UpperCAmelCase_						)
      a						:List[str] 							=       en_sentvecs.shape[0]

      # mean centering
      a						:Union[str, Any] 							=       en_sentvecs - np.mean(UpperCAmelCase_ ,  axis=0						)
      a						:int 							=       in_sentvecs - np.mean(UpperCAmelCase_ ,  axis=0						)

      a						:Optional[int] 							=       cdist(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''cosine'''						)
      a						:str 							=       np.array(range(UpperCAmelCase_						)						)
      a						:Any 							=       sim.argsort(axis=1						)[:, :10]
      a						:str 							=       np.any(preds == actual[:, None] ,  axis=1						)
      return float(matches.mean()						)







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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.config_name not in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "cvit-mkb-clsr",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
           "wiki-ner",
       ]:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
                 '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
                 '''"wiki-ner"]'''    )
       return datasets.MetricInfo(
           description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''int64'''    )
                   if self.config_name != '''cvit-mkb-clsr'''
                   else datasets.Sequence(datasets.Value('''float32'''    )    ),
                   '''references''': datasets.Value('''int64'''    )
                   if self.config_name != '''cvit-mkb-clsr'''
                   else datasets.Sequence(datasets.Value('''float32'''    )    ),
               }    )					,  codebase_urls=[]					,  reference_urls=[]					,  format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None					,  )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.config_name == "cvit-mkb-clsr":
             return {"precision@10": precision_at_aa(_lowerCamelCase					,  _lowerCamelCase    )}
       elif self.config_name in ["wiki-ner"]:
             return acc_and_fa(_lowerCamelCase					,  _lowerCamelCase    )
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
             return {"accuracy": simple_accuracy(_lowerCamelCase					,  _lowerCamelCase    )}
       else:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
                 '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
                 '''"wiki-ner"]'''    )









from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		list[str] | None = None ,  UpperCAmelCase_						:		dict[str, float] | None = None ,  UpperCAmelCase_						:		bool = False ,  ):




      """simple docstring"""





      a						:str 							=       cipher_alphabet or [chr(UpperCAmelCase_						) for i in range(97 ,  123						)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            a						:List[Any] 							=       {
                '''a''': 0.08497,
                '''b''': 0.01492,
                '''c''': 0.02202,
                '''d''': 0.04253,
                '''e''': 0.11162,
                '''f''': 0.02228,
                '''g''': 0.02015,
                '''h''': 0.06094,
                '''i''': 0.07546,
                '''j''': 0.00153,
                '''k''': 0.01292,
                '''l''': 0.04025,
                '''m''': 0.02406,
                '''n''': 0.06749,
                '''o''': 0.07507,
                '''p''': 0.01929,
                '''q''': 0.00095,
                '''r''': 0.07587,
                '''s''': 0.06327,
                '''t''': 0.09356,
                '''u''': 0.02758,
                '''v''': 0.00978,
                '''w''': 0.02560,
                '''x''': 0.00150,
                '''y''': 0.01994,
                '''z''': 0.00077,
            }
      else:
            # Custom frequencies dictionary
            a						:Dict 							=       frequencies_dict

      if not case_sensitive:
            a						:Union[str, Any] 							=       ciphertext.lower()

      # Chi squared statistic values
      a						:dict[int, tuple[float, str]] 							=       {}

      # cycle through all of the shifts
      for shift in range(len(UpperCAmelCase_						)						):
            a						:int 							=       ''''''

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        a						:Dict 							=       (alphabet_letters.index(letter.lower()						) - shift) % len(
                            UpperCAmelCase_						)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            a						:List[Any] 							=       0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        a						:Optional[int] 							=       letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:List[Any] 							=       decrypted_with_shift.lower().count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Dict 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Any 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              a						:int 							=       decrypted_with_shift.count(UpperCAmelCase_						)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              a						:Tuple 							=       frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              a						:Optional[Any] 							=       ((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            a						:Optional[Any] 							=       (
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(UpperCAmelCase_						:		int						) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      a						:int 							=       min(
          UpperCAmelCase_ ,  key=UpperCAmelCase_ ,  )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          a
      )							,				(
          a
      )							,				
      )						:Optional[int] 							=       chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )





