


from typing import List

from packaging import version
from sklearn.metrics import fa_score

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


# TODO(Patrick/Anton)
__lowerCamelCase   	=  '''\\n'''

__lowerCamelCase   	=  '''\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'''

__lowerCamelCase   	=  '''\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'''

__lowerCamelCase   	=  ['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
__lowerCamelCase   	=  ''''''

try:
       from jiwer import transforms as tr

       __lowerCamelCase   	=  True
except ImportError:
       __lowerCamelCase   	=  False

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


       class 	a__						(    tr.AbstractTransform     ):

              def __init__( self   :  int   ,				lowerCamelCase_   :  str = " "     ):
                   a_     :  int				      =     sentence_delimiter

              def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str     ):
                   return list(UpperCAmelCase__     )

              def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  List[str]     ):
                   a_     :  Union[str, Any]				      =     []
                   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

       __lowerCamelCase   	=  tr.Compose(
           [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
       )
elif _jiwer_available:
       __lowerCamelCase   	=  tr.Compose(
           [
               tr.RemoveMultipleSpaces(),
               tr.Strip(),
               tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
               tr.ReduceToListOfListOfChars(),
           ]
       )
else:
       __lowerCamelCase   	=  None



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     return float((preds == labels).mean()			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     return {
         "f1": float(fa_score(y_true=UpperCamelCase_	,	y_pred=UpperCamelCase_	,	average="""macro"""			)			),
         "accuracy": simple_accuracy(UpperCamelCase_	,	UpperCamelCase_			),
     }



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase="exp"	,	__UpperCamelCase=None	,	__UpperCamelCase=False	,	__UpperCamelCase=False	,	__UpperCamelCase=None	,	__UpperCamelCase=False	,	):
     a_     :  Dict				      =     [[label] for label in labels]
     a_     :  Union[str, Any]				      =     list(UpperCamelCase_			)
     try:
          import sacrebleu as scb
     except ImportError:
          raise ValueError(
              """sacrebleu has to be installed in order to apply the bleu metric for covost2."""
              """You can install it via `pip install sacrebleu`."""			)

     if version.parse(scb.__version__			) < version.parse("""1.4.12"""			):
          raise ImportWarning(
              """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
              """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""			)

     a_     :  int				      =     len(labels[0]			)
     if any(len(UpperCamelCase_			) != references_per_prediction for refs in labels			):
          raise ValueError("""Sacrebleu requires the same number of references for each prediction"""			)
     a_     :  List[str]				      =     [[refs[i] for refs in labels] for i in range(UpperCamelCase_			)]
     a_     :  Dict				      =     scb.corpus_bleu(
         UpperCamelCase_	,	UpperCamelCase_	,	smooth_method=UpperCamelCase_	,	smooth_value=UpperCamelCase_	,	force=UpperCamelCase_	,	lowercase=UpperCamelCase_	,	use_effective_order=UpperCamelCase_	,	**({"""tokenize""": tokenize} if tokenize else {})	,	)
     return {"bleu": output.score}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):


     try:
          from jiwer import compute_measures
     except ImportError:
          raise ValueError(
              F'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
              """You can install it via `pip install jiwer`."""			)

     if concatenate_texts:
          a_     :  Union[str, Any]				      =     compute_measures(UpperCamelCase_	,	UpperCamelCase_			)["""wer"""]

          a_     :  Union[str, Any]				      =     compute_measures(UpperCamelCase_	,	UpperCamelCase_	,	truth_transform=UpperCamelCase_	,	hypothesis_transform=UpperCamelCase_			)["""wer"""]
          return {"wer": wer, "cer": cer}
     else:

          def compute_score(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase="wer"			):
               a_     :  Optional[Any]				      =     0
               a_     :  int				      =     0
               for prediction, reference in zip(UpperCamelCase_	,	UpperCamelCase_			):
                    if score_type == "wer":
                         a_     :  Union[str, Any]				      =     compute_measures(UpperCamelCase_	,	UpperCamelCase_			)
                    elif score_type == "cer":
                         a_     :  List[str]				      =     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

          return {"wer": compute_score(UpperCamelCase_	,	UpperCamelCase_	,	"""wer"""			), "cer": compute_score(UpperCamelCase_	,	UpperCamelCase_	,	"""cer"""			)}


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

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            if self.config_name not in _CONFIG_NAMES:
                 raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''     )

            a_     :  Optional[Any]				      =     """int64""" if self.config_name in ["""fleurs-lang_id""", """minds14"""] else """string"""

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                    {"""predictions""": datasets.Value(UpperCAmelCase__     ), """references""": datasets.Value(UpperCAmelCase__     )}     )   ,				codebase_urls=[]   ,				reference_urls=[]   ,				format="""numpy"""   ,				)

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  List[str]=None   ,				lowerCamelCase_   :  List[str]=None     ):
            a_     :  Optional[Any]				      =     bleu_kwargs if bleu_kwargs is not None else {}
            a_     :  str				      =     wer_kwargs if wer_kwargs is not None else {}

            if self.config_name == "fleurs-lang_id":
                 return {"accuracy": simple_accuracy(UpperCAmelCase__   ,				UpperCAmelCase__     )}
            elif self.config_name == "minds14":
                 return fa_and_simple_accuracy(UpperCAmelCase__   ,				UpperCAmelCase__     )
            elif self.config_name == "covost2":
                 a_     :  Optional[Any]				      =     bleu_kwargs.pop("""smooth_method"""   ,				"""exp"""     )
                 a_     :  Optional[Any]				      =     bleu_kwargs.pop("""smooth_value"""   ,				UpperCAmelCase__     )
                 a_     :  List[str]				      =     bleu_kwargs.pop("""force"""   ,				UpperCAmelCase__     )
                 a_     :  List[Any]				      =     bleu_kwargs.pop("""lowercase"""   ,				UpperCAmelCase__     )
                 a_     :  Union[str, Any]				      =     bleu_kwargs.pop("""tokenize"""   ,				UpperCAmelCase__     )
                 a_     :  int				      =     bleu_kwargs.pop("""use_effective_order"""   ,				UpperCAmelCase__     )
                 return bleu(
                     preds=UpperCAmelCase__   ,				labels=UpperCAmelCase__   ,				smooth_method=UpperCAmelCase__   ,				smooth_value=UpperCAmelCase__   ,				force=UpperCAmelCase__   ,				lowercase=UpperCAmelCase__   ,				tokenize=UpperCAmelCase__   ,				use_effective_order=UpperCAmelCase__   ,				)
            elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
                 a_     :  Tuple				      =     wer_kwargs.pop("""concatenate_texts"""   ,				UpperCAmelCase__     )
                 return wer_and_cer(UpperCAmelCase__   ,				UpperCAmelCase__   ,				UpperCAmelCase__   ,				self.config_name     )
            else:
                 raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''     )






"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_a					 =				'\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

_a					 =				'\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

_a					 =				'\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'




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







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    return datasets.MetricInfo(
        description=_DESCRIPTION,   citation=_CITATION,   homepage="https://github.com/chakki-works/seqeval",   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
            {
                "predictions": datasets.Sequence(datasets.Value("string",   id="label"      ),   id="sequence"      ),
                "references": datasets.Sequence(datasets.Value("string",   id="label"      ),   id="sequence"      ),
            }      ),   codebase_urls=["https://github.com/chakki-works/seqeval"],   reference_urls=["https://github.com/chakki-works/seqeval"],   )



 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		classification_report(
        y_true=UpperCAmelCase__,   y_pred=UpperCAmelCase__,   suffix=UpperCAmelCase__,   output_dict=UpperCAmelCase__,   scheme=UpperCAmelCase__,   mode=UpperCAmelCase__,   sample_weight=UpperCAmelCase__,   zero_division=UpperCAmelCase__,   )
    report.pop("macro avg"      )
    report.pop("weighted avg"      )
    __lowercase								=		report.pop("micro avg"      )

    __lowercase								=		{
        type_name: {
            "precision": score["precision"],
            "recall": score["recall"],
            "f1": score["f1-score"],
            "number": score["support"],
        }
        for type_name, score in report.items()
    }
    __lowercase								=		overall_score["precision"]
    __lowercase								=		overall_score["recall"]
    __lowercase								=		overall_score["f1-score"]
    __lowercase								=		accuracy_score(y_true=UpperCAmelCase__,   y_pred=UpperCAmelCase__      )

    return scores




