


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   							=						''''''


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



    class 	__lowerCamelCase			(tr.AbstractTransform						):







           def __init__(			self: Tuple,A_: str = " "					):






                '''simple docstring'''




                __UpperCamelCase =			sentence_delimiter







           def 		snake_case_	(			self: Union[str, Any],A_: str					):






                '''simple docstring'''




                return list(A_					)







           def 		snake_case_	(			self: Union[str, Any],A_: List[str]					):






                '''simple docstring'''




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



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


__snake_case   							=						'''\
@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   							=						'''\
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   							=						'''
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 	__lowerCamelCase			(datasets.Metric						):







       def 		snake_case_	(			self: int					):






            '''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 		snake_case_	(			self: str,A_: int,A_: Optional[int],A_: List[Any]=False					):






            '''simple docstring'''




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

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

            return incorrect / total




"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




