




from __future__ import annotations


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							list[str] | None = None				,					a_	:							dict[str, float] | None = None				,					a_	:							bool = False				,					):
  __a			=					cipher_alphabet or [chr(lowerCamelCase_			) 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			=					{
        '''a''': 0.0_84_97,
        '''b''': 0.0_14_92,
        '''c''': 0.0_22_02,
        '''d''': 0.0_42_53,
        '''e''': 0.1_11_62,
        '''f''': 0.0_22_28,
        '''g''': 0.0_20_15,
        '''h''': 0.0_60_94,
        '''i''': 0.0_75_46,
        '''j''': 0.0_01_53,
        '''k''': 0.0_12_92,
        '''l''': 0.0_40_25,
        '''m''': 0.0_24_06,
        '''n''': 0.0_67_49,
        '''o''': 0.0_75_07,
        '''p''': 0.0_19_29,
        '''q''': 0.0_00_95,
        '''r''': 0.0_75_87,
        '''s''': 0.0_63_27,
        '''t''': 0.0_93_56,
        '''u''': 0.0_27_58,
        '''v''': 0.0_09_78,
        '''w''': 0.0_25_60,
        '''x''': 0.0_01_50,
        '''y''': 0.0_19_94,
        '''z''': 0.0_00_77,
    }
  else:
    # Custom frequencies dictionary
    __a			=					frequencies_dict

  if not case_sensitive:
    __a			=					ciphertext.lower()

  # Chi squared statistic values
  __a			=					{}

  # cycle through all of the shifts
  for shift in range(len(lowerCamelCase_			)			):
    __a			=					''''''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        __a			=					(alphabet_letters.index(letter.lower()			) - shift) % len(
            lowerCamelCase_			)
        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			=					0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        __a			=					letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          __a			=					decrypted_with_shift.lower().count(lowerCamelCase_			)

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

          # Complete the chi squared statistic formula
          __a			=					((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			=					decrypted_with_shift.count(lowerCamelCase_			)

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

          # Complete the chi squared statistic formula
          __a			=					((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			=					(
        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(a_	:							int			) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  __a			=					min(
      lowerCamelCase_				,					key=lowerCamelCase_				,					)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      __a	
  )			=					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,
  )









'''simple docstring'''


from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
  import importlib_metadata
else:
  import importlib.metadata as importlib_metadata


UpperCAmelCase_ 		=      ""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):





  class    __lowercase						(  tr.AbstractTransform ):







     def __init__(	self      ,							UpperCamelCase = " "      )				->    Tuple:
       __a			=					sentence_delimiter







     def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
       return list(UpperCamelCase      )







     def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
       __a			=					[]
       for sent_idx, sentence in enumerate(UpperCamelCase      ):
         chars.extend(self.process_string(UpperCamelCase      )      )
         if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase      ) - 1:
           chars.append(self.sentence_delimiter      )
       return chars

  UpperCAmelCase_ 		=      tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
else:
  UpperCAmelCase_ 		=      tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )


UpperCAmelCase_ 		=      "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

UpperCAmelCase_ 		=      "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCAmelCase_ 		=      "\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"





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







   def 		UpperCamelCase__			(	self      )				->    Any:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,							citation=_CITATION      ,							inputs_description=_KWARGS_DESCRIPTION      ,							features=datasets.Features(
             {
                 'predictions': datasets.Value('string'      ,							id='sequence'      ),
                 'references': datasets.Value('string'      ,							id='sequence'      ),
             }      )      ,							codebase_urls=['https://github.com/jitsi/jiwer/']      ,							reference_urls=[
             'https://en.wikipedia.org/wiki/Word_error_rate',
             'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
         ]      ,							)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=False      )				->    int:
     if concatenate_texts:
       return jiwer.compute_measures(
           UpperCamelCase      ,							UpperCamelCase      ,							truth_transform=UpperCamelCase      ,							hypothesis_transform=UpperCamelCase      ,							)["wer"]

     __a			=					0
     __a			=					0
     for prediction, reference in zip(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					jiwer.compute_measures(
           UpperCamelCase      ,							UpperCamelCase      ,							truth_transform=UpperCamelCase      ,							hypothesis_transform=UpperCamelCase      ,							)
       incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
       total += measures["substitutions"] + measures["deletions"] + measures["hits"]

     return incorrect / total



