



'''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)
A			:   List[Any]			=      """\
"""

A			:   Tuple			=      """\
    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.
"""

A			:   Tuple			=      """
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}
"""

A			:   Optional[Any]			=      ["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
A			:   Any			=      """"""

try:
      from jiwer import transforms as tr

      A			:   Optional[Any]			=      True
except ImportError:
      A			:   Union[str, Any]			=      False

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





      class 							lowerCAmelCase_		(	tr.AbstractTransform						):





          def __init__(    self    :    int,    _snake_case    :    str = " "     ):



             '''simple docstring'''







             snake_case   :							Optional[int]						  =sentence_delimiter





          def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



             '''simple docstring'''







             return list(_snake_case     )






          def 					__snake_case       (    self    :    str,    _snake_case    :    List[str]     ):



             '''simple docstring'''







             snake_case   :							Optional[Any]						  =[]
             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			:   Union[str, Any]			=      tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      A			:   Union[str, Any]			=      tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      A			:   List[str]			=      None
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   return float((preds == labels).mean()							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   return {
       "f1": float(fa_score(y_true=lowerCamelCase_					,						y_pred=lowerCamelCase_					,						average='''macro'''							)							),
       "accuracy": simple_accuracy(lowerCamelCase_					,						lowerCamelCase_							),
   }
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_="exp"					,						lowerCamelCase_=None					,						lowerCamelCase_=False					,						lowerCamelCase_=False					,						lowerCamelCase_=None					,						lowerCamelCase_=False					,						):
   # xtreme-s can only have one label
   snake_case   :							Union[str, Any]						  =[[label] for label in labels]
   snake_case   :							Dict						  =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"`.'''							)

   snake_case   :							List[Any]						  =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'''							)
   snake_case   :							Optional[Any]						  =[[refs[i] for refs in labels] for i in range(lowerCamelCase_							)]
   snake_case   :							Tuple						  =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 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   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   :							Dict						  =compute_measures(lowerCamelCase_					,						lowerCamelCase_							)['''wer''']

      snake_case   :							Any						  =compute_measures(lowerCamelCase_					,						lowerCamelCase_					,						truth_transform=lowerCamelCase_					,						hypothesis_transform=lowerCamelCase_							)['''wer''']
      return {"wer": wer, "cer": cer}
   else:

      def compute_score(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_="wer"							):
         snake_case   :							List[str]						  =0
         snake_case   :							List[Any]						  =0
         for prediction, reference in zip(lowerCamelCase_					,						lowerCamelCase_							):
            if score_type == "wer":
               snake_case   :							str						  =compute_measures(lowerCamelCase_					,						lowerCamelCase_							)
            elif score_type == "cer":
               snake_case   :							List[str]						  =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 							lowerCAmelCase_		(	datasets.Metric						):





    def 					__snake_case       (    self    :    int     ):



       '''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   :							List[str]						  ='''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 					__snake_case       (    self    :    int,    _snake_case    :    str,    _snake_case    :    Tuple,    _snake_case    :    Optional[Any]=None,    _snake_case    :    Optional[int]=None     ):



       '''simple docstring'''







       snake_case   :							List[str]						  =bleu_kwargs if bleu_kwargs is not None else {}
       snake_case   :							Union[str, Any]						  =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":
          snake_case   :							List[Any]						  =bleu_kwargs.pop('''smooth_method''',    '''exp'''     )
          snake_case   :							Union[str, Any]						  =bleu_kwargs.pop('''smooth_value''',    _snake_case     )
          snake_case   :							List[str]						  =bleu_kwargs.pop('''force''',    _snake_case     )
          snake_case   :							str						  =bleu_kwargs.pop('''lowercase''',    _snake_case     )
          snake_case   :							Optional[int]						  =bleu_kwargs.pop('''tokenize''',    _snake_case     )
          snake_case   :							Any						  =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"]:
          snake_case   :							Optional[int]						  =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}'''     )










'''simple docstring'''





import doctest

import projectq
from projectq.ops import H, Measure
def 		_a   (  lowerCamelCase_							):
   snake_case   :							str						  =quantum_engine.allocate_qubit()
   H | qubit
   Measure | qubit
   return int(lowerCamelCase_							)


if __name__ == "__main__":
      doctest.testmod()

      # initialises a new quantum backend
      A			:   Dict			=      projectq.MainEngine()

      # Generate a list of 10 random numbers
      A			:   Dict			=      [get_random_number(quantum_engine) for _ in range(10)]

      # Flushes the quantum engine from memory
      quantum_engine.flush()

      print("""Random numbers""", random_numbers_list)





