


from __future__ import annotations

from typing import Any







class _lowerCAmelCase       (   __A			):
   pass







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Any         =					data
       snake_case					:   Node | None         =					None







   def __iter__(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   str         =					self
       snake_case					:   str         =					[]
       while node:
           if node in visited:
               raise ContainsLoopError
           visited.append(UpperCamelCase__		)
           yield node.data
           snake_case					:   int         =					node.next_node







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       try:
           list(self		)
           return False
       except ContainsLoopError:
           return True


if __name__ == "__main__":
  __snake_case         =					Node(1)
  __snake_case         =					Node(2)
  __snake_case         =					Node(3)
  __snake_case         =					Node(4)
  print(root_node.has_loop)  # False
  __snake_case         =					root_node.next_node
  print(root_node.has_loop)  # True

  __snake_case         =					Node(5)
  __snake_case         =					Node(6)
  __snake_case         =					Node(5)
  __snake_case         =					Node(6)
  print(root_node.has_loop)  # False

  __snake_case         =					Node(1)
  print(root_node.has_loop)  # False









"""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


__snake_case         =					""""""


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







  class _lowerCAmelCase       (   tr.AbstractTransform			):







     def __init__(			self		,			UpperCamelCase__ = " "		)			->	Any:

         '''simple docstring'''




         snake_case					:   Optional[Any]         =					sentence_delimiter







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

         '''simple docstring'''




         return list(UpperCamelCase__		)







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

         '''simple docstring'''




         snake_case					:   Dict         =					[]
         for sent_idx, sentence in enumerate(UpperCamelCase__		):
             chars.extend(self.process_string(UpperCamelCase__		)		)
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase__		) - 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 				lowerCamelCase	(			self		)			->	Optional[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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=False		)			->	Optional[int]:

       '''simple docstring'''




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

       snake_case					:   Optional[int]         =					0
       snake_case					:   int         =					0
       for prediction, reference in zip(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Dict         =					jiwer.compute_measures(
               UpperCamelCase__		,			UpperCamelCase__		,			truth_transform=UpperCamelCase__		,			hypothesis_transform=UpperCamelCase__		,			)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total





