





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)
__A       : Any						    = "\\n"

__A       : int						    = "\\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"

__A       : Union[str, Any]						    = "\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"

__A       : Union[str, Any]						    = ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__A       : List[str]						    = ""

try:
 from jiwer import transforms as tr

 __A       : Dict						    = True
except ImportError:
 __A       : Tuple						    = False

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







 class     lowerCamelCase(	tr.AbstractTransform  ):
      '''simple docstring'''


      def __init__(						self							,    snake_case_ = " "       ):
          _A    =       sentence_delimiter




      def 					lowerCAmelCase__		(						self							,    snake_case_       ):
          return list(snake_case_       )




      def 					lowerCAmelCase__		(						self							,    snake_case_       ):
          _A    =       []
          for sent_idx, sentence in enumerate(snake_case_       ):
              chars.extend(self.process_string(snake_case_       )       )
              if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case_       ) - 1:
                  chars.append(self.sentence_delimiter       )
          return chars




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





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Union[str, Any]:




    """simple docstring"""




    return float((preds == labels).mean()							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Any:




    """simple docstring"""




    return {
        "f1": float(fa_score(y_true=_SCREAMING_SNAKE_CASE       ,    y_pred=_SCREAMING_SNAKE_CASE       ,    average='macro'							)							),
        "accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							),
    }





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE="exp"       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=False       ,    _SCREAMING_SNAKE_CASE=False       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=False       ,    )       ->   int:




    """simple docstring"""




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





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Union[str, Any]:




    """simple docstring"""




    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    =       compute_measures(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)['wer']

        _A    =       compute_measures(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    truth_transform=_SCREAMING_SNAKE_CASE       ,    hypothesis_transform=_SCREAMING_SNAKE_CASE							)['wer']
        return {"wer": wer, "cer": cer}
    else:

        def compute_score(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE="wer"							):
            _A    =       0
            _A    =       0
            for prediction, reference in zip(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							):
                if score_type == "wer":
                    _A    =       compute_measures(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
                elif score_type == "cer":
                    _A    =       compute_measures(
                        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    truth_transform=_SCREAMING_SNAKE_CASE       ,    hypothesis_transform=_SCREAMING_SNAKE_CASE							)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total

        return {"wer": compute_score(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    'wer'							), "cer": compute_score(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    'cer'							)}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         if self.config_name not in _CONFIG_NAMES:
             raise KeyError(F"You should supply a configuration name selected in {_CONFIG_NAMES}"       )

         _A    =       '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(snake_case_       ), 'references': datasets.Value(snake_case_       )}       )							,    codebase_urls=[]							,    reference_urls=[]							,    format='numpy'							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=None							,    snake_case_=None       ):
         _A    =       bleu_kwargs if bleu_kwargs is not None else {}
         _A    =       wer_kwargs if wer_kwargs is not None else {}

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












from ..utils import DummyObject, requires_backends


__A       : str						    = None





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    requires_backends(_SCREAMING_SNAKE_CASE       ,    ['sentencepiece', 'tokenizers']							)





