


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	: Optional[Any]  				=''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
						class        _lowercase      (			tr.AbstractTransform       ):






										'''simple docstring'''



										def __init__(       self			:int			,			lowerCAmelCase__			:str = " "	)		->			Optional[Any]:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		sentence_delimiter


										def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:str	)		->			List[str]:
																return list(lowercase_	)



										def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:List[str]	)		->			Union[str, Any]:
																__SCREAMING_SNAKE_CASE						:     str					=		[]
																for sent_idx, sentence in enumerate(lowercase_	):
																						chars.extend(self.process_string(lowercase_	)	)
																						if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase_	) - 1:
																												chars.append(self.sentence_delimiter	)
																return chars




						__lowerCAmelCase	: List[str]  				=tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
else:
						__lowerCAmelCase	: List[str]  				=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	: Union[str, Any]  				='\\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	: Dict  				='\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       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Tuple	)		->			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 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Union[str, Any]=False	)		->			Union[str, Any]:
										if concatenate_texts:
																return jiwer.compute_measures(
																    lowercase_			,			lowercase_			,			truth_transform=lowercase_			,			hypothesis_transform=lowercase_			,			)["wer"]

										__SCREAMING_SNAKE_CASE						:     List[str]					=		0
										__SCREAMING_SNAKE_CASE						:     Dict					=		0
										for prediction, reference in zip(lowercase_			,			lowercase_	):
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		jiwer.compute_measures(
																    lowercase_			,			lowercase_			,			truth_transform=lowercase_			,			hypothesis_transform=lowercase_			,			)
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]

										return incorrect / total

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase  							=						TypeVar('''T''')



class   snake_case_       (		Generic[T]       ):
   __A	:		deque[T]  # Cache store of keys
   __A	:		set[T]  # References of the keys in cache
   __A	:		int         =				10  # Maximum capacity of cache






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int  )					->						None:
       lowercase__ :   int    					=				deque()
       lowercase__ :   str    					=				set()
       if not n:
           lowercase__ :   str    					=				sys.maxsize
       elif n < 0:
           raise ValueError("n should be an integer greater than 0."  )
       else:
           lowercase__ :   List[Any]    					=				n






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     T  )					->						None:
       if x not in self.key_reference:
           if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
               lowercase__ :   Dict    					=				self.dq_store.pop()
               self.key_reference.remove(lowercase_  )
       else:
           self.dq_store.remove(lowercase_  )

       self.dq_store.appendleft(lowercase_  )
       self.key_reference.add(lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						None:
       for k in self.dq_store:
           print(lowercase_  )







   def __repr__(      self				:     Optional[int]  )					->						str:
       return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase  							=						LRUCache(4)
      lru_cache.refer('''A''')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('''A''')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



