



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


UpperCAmelCase							 =					""""""


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

	class  __snake_case      (   tr.AbstractTransform):







								'''simple docstring'''






								def __init__(							self  ,  a_ = " "		):
												a__				      =		sentence_delimiter


								def _a      (							self  ,  a_		):
												return list(a_		)







								def _a      (							self  ,  a_		):
												a__				      =		[]
												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




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


UpperCAmelCase							 =					"""\
@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.}
}
"""

UpperCAmelCase							 =					"""\
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.
"""

UpperCAmelCase							 =					"""
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  __snake_case      (   datasets.Metric):







							'''simple docstring'''






							def _a      (							self		):
											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 _a      (							self  ,  a_  ,  a_  ,  a_=False		):
											if concatenate_texts:
															return jiwer.compute_measures(
															    a_  ,  a_  ,  truth_transform=a_  ,  hypothesis_transform=a_  ,  )["wer"]

											a__				      =		0
											a__				      =		0
											for prediction, reference in zip(a_  ,  a_		):
															a__				      =		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






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

