

import math
from collections.abc import Callable





def 	__a     (      A__			:  Callable[[float], float]			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   xa
    SCREAMING_SNAKE_CASE										=   xa
    while True:
        if x_n == x_na or function(A__       ) == function(A__       ):
            raise ZeroDivisionError("float division by zero, could not find root"       )
        SCREAMING_SNAKE_CASE										=   x_na - (
            function(A__       ) / ((function(A__       ) - function(A__       )) / (x_na - x_n))
        )
        if abs(x_na - x_na       ) < 10**-5:
            return x_na
        SCREAMING_SNAKE_CASE										=   x_na
        SCREAMING_SNAKE_CASE										=   x_na





def 	__a     (      A__			:  float       ):
    return math.pow(A__			,  3       ) - (2 * x) - 5


if __name__ == "__main__":
    print(intersection(f, 3, 3.5))

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


__A							=''''''


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




			class        _SCREAMING_SNAKE_CASE	( tr.AbstractTransform		):



				def __init__(       self		,    lowercase = " "					)							->			List[str]:
								lowerCamelCase_         =     sentence_delimiter



				def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Optional[int]:
								return list(lowercase					)



				def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			List[str]:
								lowerCamelCase_         =     []
								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



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


__A							='''\
@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.}
}
'''

__A							='''\
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.
'''

__A							='''
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        _SCREAMING_SNAKE_CASE	( datasets.Metric		):



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, 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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=False					)							->			List[str]:
					if concatenate_texts:
									return jiwer.compute_measures(
									    lowercase		,    lowercase		,    truth_transform=lowercase		,    hypothesis_transform=lowercase		,    )["wer"]

					lowerCamelCase_         =     0
					lowerCamelCase_         =     0
					for prediction, reference in zip(lowercase		,    lowercase					):
									lowerCamelCase_         =     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


