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      :				Optional[Any]     			="\\n"

lowerCAmelCase      :				Optional[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"

lowerCAmelCase      :				Tuple     			="\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      :				List[Any]     			=["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
lowerCAmelCase      :				Optional[int]     			=""

try:
    from jiwer import transforms as tr

    lowerCAmelCase      :				Any     			=True
except ImportError:
    lowerCAmelCase      :				Tuple     			=False

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




    class 						__snake_case						(			tr.AbstractTransform		):





          '''simple docstring'''






          def __init__(       self  :					Tuple     ,	_UpperCamelCase  :					str = " ")					->Any:


              """simple docstring"""


              _lowerCamelCase  :		str			    =    sentence_delimiter



          def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str)					->Optional[int]:


              """simple docstring"""


              return list(a_)




          def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					List[str])					->int:


              """simple docstring"""


              _lowerCamelCase  :		Optional[Any]			    =    []
              for sent_idx, sentence in enumerate(a_):
                  chars.extend(self.process_string(a_))
                  if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(a_) - 1:
                      chars.append(self.sentence_delimiter)
              return chars





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







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    return float((preds == labels).mean()						)







def     A__	(       __A ,			__A						):







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







def     A__	(       __A ,			__A ,			__A="exp" ,			__A=None ,			__A=False ,			__A=False ,			__A=None ,			__A=False ,			):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    [[label] for label in labels]
    _lowerCamelCase  :		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\"`."""						)

    _lowerCamelCase  :		Dict			    =    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"""						)
    _lowerCamelCase  :		Union[str, Any]			    =    [[refs[i] for refs in labels] for i in range(_UpperCAmelCase						)]
    _lowerCamelCase  :		Tuple			    =    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__	(       __A ,			__A ,			__A ,			__A						):







    '''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:
        _lowerCamelCase  :		int			    =    compute_measures(_UpperCAmelCase ,			_UpperCAmelCase						)['''wer''']

        _lowerCamelCase  :		List[Any]			    =    compute_measures(_UpperCAmelCase ,			_UpperCAmelCase ,			truth_transform=_UpperCAmelCase ,			hypothesis_transform=_UpperCAmelCase						)['''wer''']
        return {"wer": wer, "cer": cer}
    else:

        def compute_score(__A ,			__A ,			__A="wer"						):
            _lowerCamelCase  :		List[Any]			    =    0
            _lowerCamelCase  :		Tuple			    =    0
            for prediction, reference in zip(_UpperCAmelCase ,			_UpperCAmelCase						):
                if score_type == "wer":
                    _lowerCamelCase  :		Optional[int]			    =    compute_measures(_UpperCAmelCase ,			_UpperCAmelCase						)
                elif score_type == "cer":
                    _lowerCamelCase  :		Any			    =    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 						__snake_case						(			datasets.Metric		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Union[str, Any]:


          """simple docstring"""


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

          _lowerCamelCase  :		Optional[int]			    =    '''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(a_), """references""": datasets.Value(a_)})     ,	codebase_urls=[]     ,	reference_urls=[]     ,	format="""numpy"""     ,	)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					Optional[int]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str=None     ,	_UpperCamelCase  :					str=None)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    bleu_kwargs if bleu_kwargs is not None else {}
          _lowerCamelCase  :		Any			    =    wer_kwargs if wer_kwargs is not None else {}

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__A       ={
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2Text2ForCausalLM",
				    "Speech2Text2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
				from .processing_speech_to_text_a import SpeechaTextaProcessor
				from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text_a import (
								    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextaForCausalLM,
								    SpeechaTextaPreTrainedModel,
								)

else:
				import sys

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





