






"""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)
_snake_case					   =     '\\n'

_snake_case					   =     '\\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'

_snake_case					   =     '\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'

_snake_case					   =     ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_snake_case					   =     ''

try:
 from jiwer import transforms as tr

 _snake_case					   =     True
except ImportError:
 _snake_case					   =     False

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






 class  UpperCamelCase    (			tr.AbstractTransform   ):


  def __init__(  self	:		Dict							,		UpperCAmelCase__	:		str = " "							)      -> Any:
    _a						: List[str]    =			sentence_delimiter


  def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		str							)      -> int:
    return list(UpperCAmelCase__							)







  def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		List[str]							)      -> Any:
    _a						: 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







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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__="exp"  ,     UpperCamelCase__=None  ,     UpperCamelCase__=False  ,     UpperCamelCase__=False  ,     UpperCamelCase__=None  ,     UpperCamelCase__=False  ,     ):



  '''simple docstring'''
  # xtreme-s can only have one label
  _a						: List[str]    =			[[label] for label in labels]
  _a						: Dict    =			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\"`."""       )

  _a						: int    =			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"""       )
  _a						: Optional[int]    =			[[refs[i] for refs in labels] for i in range(UpperCamelCase__       )]
  _a						: List[str]    =			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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''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						: List[str]    =			compute_measures(UpperCamelCase__  ,     UpperCamelCase__       )["""wer"""]

    _a						: Optional[Any]    =			compute_measures(UpperCamelCase__  ,     UpperCamelCase__  ,     truth_transform=UpperCamelCase__  ,     hypothesis_transform=UpperCamelCase__       )["""wer"""]
    return {"wer": wer, "cer": cer}
  else:

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


 def _lowercase			(  self	:		str							)      -> Tuple:
   if self.config_name not in _CONFIG_NAMES:
     raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""							)

   _a						: Tuple    =			"""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 _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		Any							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		List[Any]=None							,		UpperCAmelCase__	:		Tuple=None							)      -> List[str]:
   _a						: Any    =			bleu_kwargs if bleu_kwargs is not None else {}
   _a						: 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":
     _a						: Tuple    =			bleu_kwargs.pop("""smooth_method"""							,		"""exp"""							)
     _a						: Union[str, Any]    =			bleu_kwargs.pop("""smooth_value"""							,		UpperCAmelCase__							)
     _a						: int    =			bleu_kwargs.pop("""force"""							,		UpperCAmelCase__							)
     _a						: Union[str, Any]    =			bleu_kwargs.pop("""lowercase"""							,		UpperCAmelCase__							)
     _a						: Union[str, Any]    =			bleu_kwargs.pop("""tokenize"""							,		UpperCAmelCase__							)
     _a						: List[str]    =			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"]:
     _a						: 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 __future__ import annotations

import math
from itertools import permutations

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5  ,     int(math.sqrt(UpperCamelCase__       ) + 1       )  ,     6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def     lowerCAmelCase__  (  UpperCamelCase__ = 7       ):



  '''simple docstring'''
  _a						: Tuple    =			"""""".join(str(UpperCamelCase__       ) for i in range(1  ,     n + 1       )       )
  _a						: str    =			[int("""""".join(UpperCamelCase__       )       ) for i in permutations(UpperCamelCase__  ,     UpperCamelCase__       )]
  _a						: Union[str, Any]    =			[num for num in perm_list if is_prime(UpperCamelCase__       )]
  return max(UpperCamelCase__       ) if pandigitals else 0


if __name__ == "__main__":
 print(F'''{solution() = }''')

