



from typing import TYPE_CHECKING

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


_lowerCamelCase         =	{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """Constraint""",
          """ConstraintListState""",
          """DisjunctiveConstraint""",
          """PhrasalConstraint""",
      ]
      _lowerCamelCase         =	[
          """BeamHypotheses""",
          """BeamScorer""",
          """BeamSearchScorer""",
          """ConstrainedBeamSearchScorer""",
      ]
      _lowerCamelCase         =	[
          """EpsilonLogitsWarper""",
          """EtaLogitsWarper""",
          """ForcedBOSTokenLogitsProcessor""",
          """ForcedEOSTokenLogitsProcessor""",
          """HammingDiversityLogitsProcessor""",
          """InfNanRemoveLogitsProcessor""",
          """LogitsProcessor""",
          """LogitsProcessorList""",
          """LogitsWarper""",
          """MinLengthLogitsProcessor""",
          """MinNewTokensLengthLogitsProcessor""",
          """NoBadWordsLogitsProcessor""",
          """NoRepeatNGramLogitsProcessor""",
          """PrefixConstrainedLogitsProcessor""",
          """RepetitionPenaltyLogitsProcessor""",
          """SequenceBiasLogitsProcessor""",
          """EncoderRepetitionPenaltyLogitsProcessor""",
          """TemperatureLogitsWarper""",
          """TopKLogitsWarper""",
          """TopPLogitsWarper""",
          """TypicalLogitsWarper""",
          """EncoderNoRepeatNGramLogitsProcessor""",
          """ExponentialDecayLengthPenalty""",
          """LogitNormalization""",
      ]
      _lowerCamelCase         =	[
          """MaxNewTokensCriteria""",
          """MaxLengthCriteria""",
          """MaxTimeCriteria""",
          """StoppingCriteria""",
          """StoppingCriteriaList""",
          """validate_stopping_criteria""",
      ]
      _lowerCamelCase         =	[
          """GenerationMixin""",
          """top_k_top_p_filtering""",
          """GreedySearchEncoderDecoderOutput""",
          """GreedySearchDecoderOnlyOutput""",
          """SampleEncoderDecoderOutput""",
          """SampleDecoderOnlyOutput""",
          """BeamSearchEncoderDecoderOutput""",
          """BeamSearchDecoderOnlyOutput""",
          """BeamSampleEncoderDecoderOutput""",
          """BeamSampleDecoderOnlyOutput""",
          """ContrastiveSearchEncoderDecoderOutput""",
          """ContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """TFForcedBOSTokenLogitsProcessor""",
          """TFForcedEOSTokenLogitsProcessor""",
          """TFLogitsProcessor""",
          """TFLogitsProcessorList""",
          """TFLogitsWarper""",
          """TFMinLengthLogitsProcessor""",
          """TFNoBadWordsLogitsProcessor""",
          """TFNoRepeatNGramLogitsProcessor""",
          """TFRepetitionPenaltyLogitsProcessor""",
          """TFTemperatureLogitsWarper""",
          """TFTopKLogitsWarper""",
          """TFTopPLogitsWarper""",
          """TFForceTokensLogitsProcessor""",
          """TFSuppressTokensAtBeginLogitsProcessor""",
          """TFSuppressTokensLogitsProcessor""",
      ]
      _lowerCamelCase         =	[
          """TFGenerationMixin""",
          """tf_top_k_top_p_filtering""",
          """TFGreedySearchDecoderOnlyOutput""",
          """TFGreedySearchEncoderDecoderOutput""",
          """TFSampleEncoderDecoderOutput""",
          """TFSampleDecoderOnlyOutput""",
          """TFBeamSearchEncoderDecoderOutput""",
          """TFBeamSearchDecoderOnlyOutput""",
          """TFBeamSampleEncoderDecoderOutput""",
          """TFBeamSampleDecoderOnlyOutput""",
          """TFContrastiveSearchEncoderDecoderOutput""",
          """TFContrastiveSearchDecoderOnlyOutput""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase         =	[
          """FlaxForcedBOSTokenLogitsProcessor""",
          """FlaxForcedEOSTokenLogitsProcessor""",
          """FlaxLogitsProcessor""",
          """FlaxLogitsProcessorList""",
          """FlaxLogitsWarper""",
          """FlaxMinLengthLogitsProcessor""",
          """FlaxTemperatureLogitsWarper""",
          """FlaxTopKLogitsWarper""",
          """FlaxTopPLogitsWarper""",
      ]
      _lowerCamelCase         =	[
          """FlaxGenerationMixin""",
          """FlaxGreedySearchOutput""",
          """FlaxSampleOutput""",
          """FlaxBeamSearchOutput""",
      ]

if TYPE_CHECKING:
      from .configuration_utils import GenerationConfig
      from .streamers import TextIteratorStreamer, TextStreamer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
            from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
            from .logits_process import (
                EncoderNoRepeatNGramLogitsProcessor,
                EncoderRepetitionPenaltyLogitsProcessor,
                EpsilonLogitsWarper,
                EtaLogitsWarper,
                ExponentialDecayLengthPenalty,
                ForcedBOSTokenLogitsProcessor,
                ForcedEOSTokenLogitsProcessor,
                HammingDiversityLogitsProcessor,
                InfNanRemoveLogitsProcessor,
                LogitNormalization,
                LogitsProcessor,
                LogitsProcessorList,
                LogitsWarper,
                MinLengthLogitsProcessor,
                MinNewTokensLengthLogitsProcessor,
                NoBadWordsLogitsProcessor,
                NoRepeatNGramLogitsProcessor,
                PrefixConstrainedLogitsProcessor,
                RepetitionPenaltyLogitsProcessor,
                SequenceBiasLogitsProcessor,
                TemperatureLogitsWarper,
                TopKLogitsWarper,
                TopPLogitsWarper,
                TypicalLogitsWarper,
            )
            from .stopping_criteria import (
                MaxLengthCriteria,
                MaxNewTokensCriteria,
                MaxTimeCriteria,
                StoppingCriteria,
                StoppingCriteriaList,
                validate_stopping_criteria,
            )
            from .utils import (
                BeamSampleDecoderOnlyOutput,
                BeamSampleEncoderDecoderOutput,
                BeamSearchDecoderOnlyOutput,
                BeamSearchEncoderDecoderOutput,
                ContrastiveSearchDecoderOnlyOutput,
                ContrastiveSearchEncoderDecoderOutput,
                GenerationMixin,
                GreedySearchDecoderOnlyOutput,
                GreedySearchEncoderDecoderOutput,
                SampleDecoderOnlyOutput,
                SampleEncoderDecoderOutput,
                top_k_top_p_filtering,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tf_logits_process import (
                TFForcedBOSTokenLogitsProcessor,
                TFForcedEOSTokenLogitsProcessor,
                TFForceTokensLogitsProcessor,
                TFLogitsProcessor,
                TFLogitsProcessorList,
                TFLogitsWarper,
                TFMinLengthLogitsProcessor,
                TFNoBadWordsLogitsProcessor,
                TFNoRepeatNGramLogitsProcessor,
                TFRepetitionPenaltyLogitsProcessor,
                TFSuppressTokensAtBeginLogitsProcessor,
                TFSuppressTokensLogitsProcessor,
                TFTemperatureLogitsWarper,
                TFTopKLogitsWarper,
                TFTopPLogitsWarper,
            )
            from .tf_utils import (
                TFBeamSampleDecoderOnlyOutput,
                TFBeamSampleEncoderDecoderOutput,
                TFBeamSearchDecoderOnlyOutput,
                TFBeamSearchEncoderDecoderOutput,
                TFContrastiveSearchDecoderOnlyOutput,
                TFContrastiveSearchEncoderDecoderOutput,
                TFGenerationMixin,
                TFGreedySearchDecoderOnlyOutput,
                TFGreedySearchEncoderDecoderOutput,
                TFSampleDecoderOnlyOutput,
                TFSampleEncoderDecoderOutput,
                tf_top_k_top_p_filtering,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .flax_logits_process import (
                FlaxForcedBOSTokenLogitsProcessor,
                FlaxForcedEOSTokenLogitsProcessor,
                FlaxLogitsProcessor,
                FlaxLogitsProcessorList,
                FlaxLogitsWarper,
                FlaxMinLengthLogitsProcessor,
                FlaxTemperatureLogitsWarper,
                FlaxTopKLogitsWarper,
                FlaxTopPLogitsWarper,
            )
            from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
      import sys

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










"""simple docstring"""
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)
__SCREAMING_SNAKE_CASE				:     str		=     '\\n'

__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     '\\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'

__SCREAMING_SNAKE_CASE				:     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'

__SCREAMING_SNAKE_CASE				:     List[str]		=     ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__SCREAMING_SNAKE_CASE				:     List[str]		=     ''

try:
				from jiwer import transforms as tr

				__SCREAMING_SNAKE_CASE				:     Dict		=     True
except ImportError:
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     False

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





				class   __A  (tr.AbstractTransform):





											'''simple docstring'''

											def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   str = " "							)       ->str:



																		"""simple docstring"""



																		snake_case_     							=		sentence_delimiter



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   str							)       ->List[str]:



																		"""simple docstring"""



																		return list(UpperCAmelCase_							)



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   List[str]							)       ->Any:



																		"""simple docstring"""



																		snake_case_     							=		[]
																		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


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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							return float((preds == labels).mean()					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							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     _a					(   _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					,			)     ->					Optional[Any]:
							# xtreme-s can only have one label
							snake_case_     							=		[[label] for label in labels]
							snake_case_     							=		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\"`."""					)

							snake_case_     							=		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"""					)
							snake_case_     							=		[[refs[i] for refs in labels] for i in range(_SCREAMING_SNAKE_CASE					)]
							snake_case_     							=		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     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							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:
														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)["""wer"""]

														snake_case_     							=		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"					):
																					snake_case_     							=		0
																					snake_case_     							=		0
																					for prediction, reference in zip(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
																												if score_type == "wer":
																																			snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												elif score_type == "cer":
																																			snake_case_     							=		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   __A  (datasets.Metric):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   Any							)       ->List[str]:



														"""simple docstring"""



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

														snake_case_     							=		"""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 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[str]=None		,							UpperCAmelCase_				:   List[Any]=None							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		bleu_kwargs if bleu_kwargs is not None else {}
														snake_case_     							=		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":
																					snake_case_     							=		bleu_kwargs.pop("""smooth_method"""		,							"""exp"""							)
																					snake_case_     							=		bleu_kwargs.pop("""smooth_value"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""force"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""lowercase"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""tokenize"""		,							UpperCAmelCase_							)
																					snake_case_     							=		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"]:
																					snake_case_     							=		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}"""							)



