






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  							=  "\\n"

__A  							=  "\\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"

__A  							=  "\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"

__A  							=  ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__A  							=  ""

try:
   from jiwer import transforms as tr

   __A  							=  True
except ImportError:
   __A  							=  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			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = " "			)			->       int:
              __UpperCAmelCase    							=sentence_delimiter



       def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Tuple			)			->       str:
              return list(SCREAMING_SNAKE_CASE_			)



       def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       List[Any]:
              __UpperCAmelCase    							=[]
              for sent_idx, sentence in enumerate(SCREAMING_SNAKE_CASE_			):
                     chars.extend(self.process_string(SCREAMING_SNAKE_CASE_			)			)
                     if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(SCREAMING_SNAKE_CASE_			) - 1:
                            chars.append(self.sentence_delimiter			)
              return chars




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







def 			lowercase__  (				A_:	Optional[Any]     ,			A_:	Dict		)  ->							Optional[Any]:







       """simple docstring"""






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







def 			lowercase__  (				A_:	Any     ,			A_:	Optional[int]		)  ->							List[Any]:







       """simple docstring"""






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







def 			lowercase__  (				A_:	Dict     ,			A_:	Dict     ,			A_:	List[str]="exp"     ,			A_:	Optional[int]=None     ,			A_:	Dict=False     ,			A_:	List[str]=False     ,			A_:	Any=None     ,			A_:	List[Any]=False     ,			)  ->							str:







       """simple docstring"""






       __UpperCAmelCase    							=[[label] for label in labels]
       __UpperCAmelCase    							=list(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\"`."""		)

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







def 			lowercase__  (				A_:	Tuple     ,			A_:	List[str]     ,			A_:	Optional[int]     ,			A_:	Any		)  ->							Dict:







       """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:
              __UpperCAmelCase    							=compute_measures(snake_case__     ,			snake_case__		)['wer']

              __UpperCAmelCase    							=compute_measures(snake_case__     ,			snake_case__     ,			truth_transform=snake_case__     ,			hypothesis_transform=snake_case__		)['wer']
              return {"wer": wer, "cer": cer}
       else:

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

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





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION							)
class 							_A (    datasets.Metric							):
    """simple docstring"""

    def _a       (							self			:    Dict			)			->       Dict:
           if self.config_name not in _CONFIG_NAMES:
                  raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''			)

           __UpperCAmelCase    							='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(SCREAMING_SNAKE_CASE_			), """references""": datasets.Value(SCREAMING_SNAKE_CASE_			)}			)   ,    codebase_urls=[]   ,    reference_urls=[]   ,    format="""numpy"""   ,    )



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple=None   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=None			)			->       Union[str, Any]:
           __UpperCAmelCase    							=bleu_kwargs if bleu_kwargs is not None else {}
           __UpperCAmelCase    							=wer_kwargs if wer_kwargs is not None else {}

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












from __future__ import annotations

__A  							=  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]



class 							_A :
    """simple docstring"""

    def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       None:
           __UpperCAmelCase    							=-1
           __UpperCAmelCase    							=-1
           __UpperCAmelCase    							=[-1] * length
           __UpperCAmelCase    							=None
           __UpperCAmelCase    							=None



    def __repr__(							self			:    Optional[int]			)			->       str:
           return f'''Node(min_value={self.minn} max_value={self.maxx})'''







def 			lowercase__  (				A_:	list[int]		)  ->							Node | None:







       """simple docstring"""






       __UpperCAmelCase    							=Node(len(A_		)		)
       __UpperCAmelCase							,   __UpperCAmelCase    							=min(A_		), max(A_		)
       # Leaf node case where the node contains only one unique value
       if root.minn == root.maxx:
              return root
       __UpperCAmelCase    							=(root.minn + root.maxx) // 2

       __UpperCAmelCase    							=[]
       __UpperCAmelCase    							=[]

       for index, num in enumerate(A_		):
              if num <= pivot:
                     left_arr.append(A_		)
              else:
                     right_arr.append(A_		)
              __UpperCAmelCase    							=len(A_		)
       __UpperCAmelCase    							=build_tree(A_		)
       __UpperCAmelCase    							=build_tree(A_		)
       return root







def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int		)  ->							int:







       """simple docstring"""






       if index < 0 or node is None:
              return 0
       # Leaf node cases
       if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
       __UpperCAmelCase    							=(node.minn + node.maxx) // 2
       if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left     ,			A_     ,			node.map_left[index] - 1		)
       else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right     ,			A_     ,			index - node.map_left[index]		)







def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:







       """simple docstring"""






       if start > end:
              return 0
       __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			A_		)
       __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			start - 1		)
       return rank_till_end - rank_before_start







def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:







       """simple docstring"""






       if index > (end - start) or start > end or node is None:
              return -1
       # Leaf node case
       if node.minn == node.maxx:
              return node.minn
       # Number of elements in the left subtree in interval [start, end]
       __UpperCAmelCase    							=node.map_left[end] - (
           node.map_left[start - 1] if start else 0
       )
       if num_elements_in_left_tree > index:
              return quantile(
                  node.left     ,			A_     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			)
       else:
              return quantile(
                  node.right     ,			index - num_elements_in_left_tree     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			)







def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:







       """simple docstring"""






       if (
           start > end
           or node is None
           or start_num > end_num
           or node.minn > end_num
           or node.maxx < start_num
       ):
              return 0
       if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
       __UpperCAmelCase    							=range_counting(
           node.left     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			A_     ,			A_     ,			)
       __UpperCAmelCase    							=range_counting(
           node.right     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			A_     ,			A_     ,			)
       return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()




