

"""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


_UpperCamelCase		     =    ''


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






    class 			__a					(						tr.AbstractTransform       ):





           """simple docstring"""


           def __init__(   self			,					snake_case = " "   ):





            """simple docstring"""

            lowerCAmelCase__   :				Dict 			=				sentence_delimiter






           def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





            """simple docstring"""

            return list(lowercase__   )






           def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





            """simple docstring"""

            lowerCAmelCase__   :				List[str] 			=				[]
            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




    _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		     =    '\\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'

_UpperCamelCase		     =    '\\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'

_UpperCamelCase		     =    '\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 			__a					(						datasets.Metric       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case=False   ):





        """simple docstring"""

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

        lowerCAmelCase__   :				int 			=				0
        lowerCAmelCase__   :				Any 			=				0
        for prediction, reference in zip(lowercase__			,					lowercase__   ):
         lowerCAmelCase__   :				Tuple 			=				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












'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 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
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
