




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


lowerCamelCase__			:       Union[str, Any]         =							''


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





			class 	lowerCamelCase_							(				tr.AbstractTransform ):


				'''simple docstring'''







				def __init__(							self  :		Optional[int]    ,    _lowerCAmelCase  :		str = " "						):
										SCREAMING_SNAKE_CASE_     						=      sentence_delimiter



				def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
										return list(_lowerCAmelCase						)




				def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		List[str]						):
										SCREAMING_SNAKE_CASE_     						=      []
										for sent_idx, sentence in enumerate(_lowerCAmelCase						):
																chars.extend(self.process_string(_lowerCAmelCase						)						)
																if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_lowerCAmelCase						) - 1:
																						chars.append(self.sentence_delimiter						)
										return chars


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


lowerCamelCase__			:       Dict         =							'\\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'

lowerCamelCase__			:       Optional[int]         =							'\\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'

lowerCamelCase__			:       int         =							'\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 	lowerCamelCase_							(				datasets.Metric ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Optional[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 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Union[str, Any]=False						):
							if concatenate_texts:
													return jiwer.compute_measures(
													    _lowerCAmelCase    ,    _lowerCAmelCase    ,    truth_transform=_lowerCAmelCase    ,    hypothesis_transform=_lowerCAmelCase    ,    )["wer"]

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

							return incorrect / total





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			dict[str, str]:
      SCREAMING_SNAKE_CASE_     						=      [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE_     						=      remove_duplicates(key.upper()							)
      SCREAMING_SNAKE_CASE_     						=      len(__UpperCAmelCase							)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE_     						=      {alphabet[i]: char for i, char in enumerate(__UpperCAmelCase							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(__UpperCAmelCase							)      ,      26							):
            SCREAMING_SNAKE_CASE_     						=      alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE_     						=      alphabet[i - offset]
            SCREAMING_SNAKE_CASE_     						=      char
      return cipher_alphabet

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     dict[str, str]							) ->			str:
      return "".join(cipher_map.get(__UpperCAmelCase      ,      __UpperCAmelCase							) for ch in message.upper()							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     dict[str, str]							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(__UpperCAmelCase      ,      __UpperCAmelCase							) for ch in message.upper()							)

def  UpperCAmelCase_							(				) ->			None:
      SCREAMING_SNAKE_CASE_     						=      input('Enter message to encode or decode: '							).strip()
      SCREAMING_SNAKE_CASE_     						=      input('Enter keyword: '							).strip()
      SCREAMING_SNAKE_CASE_     						=      input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE_     						=      {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      SCREAMING_SNAKE_CASE_     						=      create_cipher_map(__UpperCAmelCase							)
      print(func(__UpperCAmelCase      ,      __UpperCAmelCase							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()