


"""simple docstring"""



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case         =					"""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

__snake_case         =					"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

__snake_case         =					"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION			)
class _lowerCAmelCase       (   datasets.Metric			):







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION		,			citation=_CITATION		,			inputs_description=_KWARGS_DESCRIPTION		,			features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("string"		,			id="token"		)		,			id="sequence"		),
                   "references": datasets.Sequence(
                       datasets.Sequence(datasets.Value("string"		,			id="token"		)		,			id="sequence"		)		,			id="references"		),
               }		)		,			codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]		,			reference_urls=[
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=4		,			UpperCamelCase__=False		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					compute_bleu(
           reference_corpus=UpperCamelCase__		,			translation_corpus=UpperCamelCase__		,			max_order=UpperCamelCase__		,			smooth=UpperCamelCase__		)
       ((snake_case)      ,(snake_case)      ,(snake_case)      ,(snake_case)      ,(snake_case)      ,(snake_case))					:   Optional[Any]         =					score
       return {
           "bleu": bleu,
           "precisions": precisions,
           "brevity_penalty": bp,
           "length_ratio": ratio,
           "translation_length": translation_length,
           "reference_length": reference_length,
       }









"""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





