



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__snake_case		:Optional[int]   	=  TypeVar('''KEY''')
__snake_case		:str   	=  TypeVar('''VAL''')

@dataclass(frozen=UpperCAmelCase__   ,slots=UpperCAmelCase__					)
class 					_A (			Generic[KEY, VAL]					):
     UpperCamelCase__						:					KEY
     UpperCamelCase__						:					VAL


class 					_A (			_Item					):



     def __init__(	self							:  Dict):


         '''simple docstring'''

         super().__init__(lowerCamelCase__  ,	lowerCamelCase__)





     def __bool__(	self							:  List[str]):


         '''simple docstring'''

         return False


__snake_case		:Optional[Any]   	=  _DeletedItem()

class 					_A (			MutableMapping[KEY, VAL]					):



     def __init__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  int = 8  ,	__SCREAMING_SNAKE_CASE							:  float = 0.75):


         '''simple docstring'''

         __a		 =       initial_block_size
         __a		 =       [None] * initial_block_size
         assert 0.0 < capacity_factor < 1.0
         __a		 =       capacity_factor
         __a		 =       0



     def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  KEY):


         '''simple docstring'''

         return hash(lowerCamelCase__) % len(self._buckets)



     def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  int):


         '''simple docstring'''

         return (ind + 1) % len(self._buckets)



     def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  KEY  ,	__SCREAMING_SNAKE_CASE							:  VAL):


         '''simple docstring'''

         __a		 =       self._buckets[ind]
         if not stored:
             __a		 =       _Item(lowerCamelCase__  ,	lowerCamelCase__)
             self._len += 1
             return True
         elif stored.key == key:
             __a		 =       _Item(lowerCamelCase__  ,	lowerCamelCase__)
             return True
         else:
             return False



     def 	_lowerCamelCase    (	self							:  List[str]):


         '''simple docstring'''

         __a		 =       len(self._buckets) * self._capacity_factor
         return len(self) >= int(lowerCamelCase__)



     def 	_lowerCamelCase    (	self							:  Optional[int]):


         '''simple docstring'''

         if len(self._buckets) <= self._initial_block_size:
             return False
         __a		 =       len(self._buckets) * self._capacity_factor / 2
         return len(self) < limit



     def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


         '''simple docstring'''

         __a		 =       self._buckets
         __a		 =       [None] * new_size
         __a		 =       0
         for item in old_buckets:
             if item:
                 self._add_item(item.key  ,	item.val)



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


         '''simple docstring'''

         self._resize(len(self._buckets) * 2)



     def 	_lowerCamelCase    (	self							:  List[str]):


         '''simple docstring'''

         self._resize(len(self._buckets) // 2)



     def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  KEY):


         '''simple docstring'''

         __a		 =       self._get_bucket_index(lowerCamelCase__)
         for _ in range(len(self._buckets)):
             yield ind
             __a		 =       self._get_next_ind(lowerCamelCase__)



     def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  KEY  ,	__SCREAMING_SNAKE_CASE							:  VAL):


         '''simple docstring'''

         for ind in self._iterate_buckets(lowerCamelCase__):
             if self._try_set(lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__):
                 break



     def __setitem__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  KEY  ,	__SCREAMING_SNAKE_CASE							:  VAL):


         '''simple docstring'''

         if self._is_full():
             self._size_up()

         self._add_item(lowerCamelCase__  ,	lowerCamelCase__)



     def __delitem__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  KEY):


         '''simple docstring'''

         for ind in self._iterate_buckets(lowerCamelCase__):
             __a		 =       self._buckets[ind]
             if item is None:
                 raise KeyError(lowerCamelCase__)
             if item is _deleted:
                 continue
             if item.key == key:
                 __a		 =       _deleted
                 self._len -= 1
                 break
         if self._is_sparse():
             self._size_down()



     def __getitem__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  KEY):


         '''simple docstring'''

         for ind in self._iterate_buckets(lowerCamelCase__):
             __a		 =       self._buckets[ind]
             if item is None:
                 break
             if item is _deleted:
                 continue
             if item.key == key:
                 return item.val
         raise KeyError(lowerCamelCase__)



     def __len__(	self							:  int):


         '''simple docstring'''

         return self._len



     def __iter__(	self							:  Optional[Any]):


         '''simple docstring'''

         yield from (item.key for item in self._buckets if item)





     def __repr__(	self							:  str):


         '''simple docstring'''

         __a		 =       " ,".join(
             F'{item.key}: {item.val}' for item in self._buckets if item)
         return F'HashMap({val_string})'










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


__snake_case		:int   	=  ''''''


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

 class 					_A (			tr.AbstractTransform					):



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


            '''simple docstring'''

            __a		 =       sentence_delimiter



        def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str):


            '''simple docstring'''

            return list(__SCREAMING_SNAKE_CASE)





        def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


            '''simple docstring'''

            __a		 =       []
            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


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


__snake_case		:Optional[int]   	=  '''\
@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.}
}
'''

__snake_case		:Tuple   	=  '''\
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.
'''

__snake_case		:Tuple   	=  '''
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 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''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 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict=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"]

           __a		 =       0
           __a		 =       0
           for prediction, reference in zip(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       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





