



"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =abs(__lowerCAmelCase     )
			lowerCamelCase__		:		List[Any]		     =0
			while n > 0:
						res += n % 10
						n //= 10
			return res






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[Any]		     =abs(__lowerCAmelCase     )
			return n if n < 10 else n % 10 + sum_of_digits(n // 10     )






def 					snake_case__       (		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			return sum(int(__lowerCAmelCase     ) for c in str(abs(__lowerCAmelCase     )     )     )






def 					snake_case__       (		):
			"""simple docstring"""
			from collections.abc import Callable
			from timeit import timeit

			def benchmark_a_function(__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   List[str]     ) -> None:
						lowerCamelCase__		:		Dict		     =f'''{func.__name__}({value})'''
						lowerCamelCase__		:		Tuple		     =timeit(f'''__main__.{call}'''							,		setup='''import __main__'''     )
						print(f'''{call:56} = {func(__lowerCAmelCase     )} -- {timing:.4f} seconds'''     )

			for value in (262144, 1125899906842624, 1267650600228229401496703205376):
						for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
									benchmark_a_function(__lowerCAmelCase							,		__lowerCAmelCase     )
						print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()









"""simple docstring"""






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


_lowercase			:			List[str]										=						""


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

		class 	__SCREAMING_SNAKE_CASE     (				tr.AbstractTransform       ):


							'''simple docstring'''





							def __init__(	self							:  List[Any],  lowerCamelCase							:  str = " "					)-> List[str]:
										lowerCamelCase__		:		List[str]		     =sentence_delimiter



							def 		snake_case	(	self							:  Any,  lowerCamelCase							:  str					)-> Optional[Any]:
										return list(lowerCamelCase					)




							def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  List[str]					)-> Tuple:
										lowerCamelCase__		:		Optional[int]		     =[]
										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





		_lowercase			:			Optional[int]										=						tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		_lowercase			:			List[str]										=						tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


_lowercase			:			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"

_lowercase			:			List[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"

_lowercase			:			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 	__SCREAMING_SNAKE_CASE     (				datasets.Metric       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Dict					)-> 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 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict=False					)-> List[Any]:
								if concatenate_texts:
											return jiwer.compute_measures(
											    lowerCamelCase,  lowerCamelCase,  truth_transform=lowerCamelCase,  hypothesis_transform=lowerCamelCase,  )["wer"]

								lowerCamelCase__		:		Optional[Any]		     =0
								lowerCamelCase__		:		Union[str, Any]		     =0
								for prediction, reference in zip(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		int		     =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




