






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[int]				=''''''


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





 class 			__a  (    tr.AbstractTransform       ):







  def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   str = " "						):


       '''simple docstring'''






       UpperCamelCase__			:   Dict       				=  sentence_delimiter







  def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   str						):


       '''simple docstring'''






       return list(SCREAMING_SNAKE_CASE						)







  def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


       '''simple docstring'''






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







 lowerCamelCase	:							List[str]				=tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 lowerCamelCase	:							List[Any]				=tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


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

lowerCamelCase	:							Dict				='''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'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
performance of the ASR system with a CER of 0 being a perfect score.
'''

lowerCamelCase	:							int				='''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''





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







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      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 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=False						):


      '''simple docstring'''






      if concatenate_texts:
           return jiwer.compute_measures(
               SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   truth_transform=SCREAMING_SNAKE_CASE							,   hypothesis_transform=SCREAMING_SNAKE_CASE							,   )["wer"]

      UpperCamelCase__			:   List[str]       				=  0
      UpperCamelCase__			:   Any       				=  0
      for prediction, reference in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   List[Any]       				=  jiwer.compute_measures(
               SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   truth_transform=SCREAMING_SNAKE_CASE							,   hypothesis_transform=SCREAMING_SNAKE_CASE							,   )
           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

lowerCamelCase	:							int				=TypeVar('''T''')





class 			__a  (    Generic[T]       ):
 _lowerCAmelCase       :       deque[T]  # Cache store of keys
 _lowerCAmelCase       :       set[T]  # References of the keys in cache
 _lowerCAmelCase       :       int        = 1_0  # Maximum capacity of cache







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  deque()
      UpperCamelCase__			:   Optional[int]       				=  set()
      if not n:
           UpperCamelCase__			:   Tuple       				=  sys.maxsize
      elif n < 0:
           raise ValueError("n should be an integer greater than 0."						)
      else:
           UpperCamelCase__			:   Dict       				=  n







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   T						):


      '''simple docstring'''






      if x not in self.key_reference:
           if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
                UpperCamelCase__			:   int       				=  self.dq_store.pop()
                self.key_reference.remove(SCREAMING_SNAKE_CASE						)
      else:
           self.dq_store.remove(SCREAMING_SNAKE_CASE						)

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







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for k in self.dq_store:
           print(SCREAMING_SNAKE_CASE						)







 def __repr__(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase	:							LRUCache[str | int]				=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]"