



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         =	"""\
"""

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

_lowerCamelCase         =	"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions 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.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

_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    _SCREAMING_SNAKE_CASE						(tr.AbstractTransform			):





           def __init__(							self			:				Tuple  ,	UpperCamelCase			:				str = " "					)->List[str]:
            __SCREAMING_SNAKE_CASE							:     List[Any]							=						sentence_delimiter





           def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				str					)->Any:
            return list(UpperCamelCase					)






           def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				List[str]					)->Tuple:
            __SCREAMING_SNAKE_CASE							:     int							=						[]
            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 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





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






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  str  ):



 """simple docstring"""





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






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Optional[int]="exp"		,						__lowerCamelCase :  Optional[Any]=None		,						__lowerCamelCase :  Tuple=False		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Dict=None		,						__lowerCamelCase :  Optional[Any]=False		,						):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[[label] for label in labels]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						list(__lowerCamelCase  )
 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\"`."  )

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  int  ):



 """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:
  __SCREAMING_SNAKE_CASE							:     Dict							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase  )["wer"]

  __SCREAMING_SNAKE_CASE							:     List[Any]							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase		,						truth_transform=__lowerCamelCase		,						hypothesis_transform=__lowerCamelCase  )["wer"]
  return {"wer": wer, "cer": cer}
 else:

  def compute_score(__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]="wer"  ):
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0
   for prediction, reference in zip(__lowerCamelCase		,						__lowerCamelCase  ):
    if score_type == "wer":
     __SCREAMING_SNAKE_CASE							:     List[str]							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase  )
    elif score_type == "cer":
     __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						compute_measures(
         __lowerCamelCase		,						__lowerCamelCase		,						truth_transform=__lowerCamelCase		,						hypothesis_transform=__lowerCamelCase  )
    incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
    total += measures["substitutions"] + measures["deletions"] + measures["hits"]
   return incorrect / total

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






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





     def 						__snake_case     (							self			:				Optional[Any]					)->Tuple:
      if self.config_name not in _CONFIG_NAMES:
       raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

      __SCREAMING_SNAKE_CASE							:     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 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Union[str, Any]=None  ,	UpperCamelCase			:				Dict=None					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     str							=						bleu_kwargs if bleu_kwargs is not None else {}
      __SCREAMING_SNAKE_CASE							:     Any							=						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":
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						bleu_kwargs.pop("smooth_method"  ,	"exp"					)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						bleu_kwargs.pop("smooth_value"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     str							=						bleu_kwargs.pop("force"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						bleu_kwargs.pop("lowercase"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						bleu_kwargs.pop("tokenize"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Dict							=						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"]:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





