





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_   =   "\\n"

lowerCAmelCase_   =   "\\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_   =   "\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_   =   ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
lowerCAmelCase_   =   ""

try:
   from jiwer import transforms as tr

   lowerCAmelCase_   =   True
except ImportError:
   lowerCAmelCase_   =   False

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

   class 				A    (tr.AbstractTransform				):



         def __init__(					self     ,  lowercase_ = " "					)      ->   Union[str, Any]:



                '''simple docstring'''

                _snake_case      :							Tuple     				=						sentence_delimiter



         def    __a (					self     ,  lowercase_					)      ->   Tuple:



                '''simple docstring'''

                return list(lowercase_					)



         def    __a (					self     ,  lowercase_					)      ->   Any:



                '''simple docstring'''

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


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




def 						A_			(  lowercase_      ,		lowercase_  )			->						int:
       return float((preds == labels).mean()  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[Any]:
       return {
           "f1": float(fa_score(y_true=lowercase_      ,		y_pred=lowercase_      ,		average='''macro'''  )  ),
           "accuracy": simple_accuracy(lowercase_      ,		lowercase_  ),
       }




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_="exp"      ,		lowercase_=None      ,		lowercase_=False      ,		lowercase_=False      ,		lowercase_=None      ,		lowercase_=False      ,		)			->						Any:
       # xtreme-s can only have one label
       _snake_case      :							Optional[Any]     				=						[[label] for label in labels]
       _snake_case      :							List[Any]     				=						list(lowercase_  )
       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      :							Optional[int]     				=						len(labels[0]  )
       if any(len(lowercase_  ) != references_per_prediction for refs in labels  ):
              raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
       _snake_case      :							List[str]     				=						[[refs[i] for refs in labels] for i in range(lowercase_  )]
       _snake_case      :							int     				=						scb.corpus_bleu(
           lowercase_      ,		lowercase_      ,		smooth_method=lowercase_      ,		smooth_value=lowercase_      ,		force=lowercase_      ,		lowercase=lowercase_      ,		use_effective_order=lowercase_      ,		**({'''tokenize''': tokenize} if tokenize else {})      ,		)
       return {"bleu": output.score}




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						str:
       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      :							Any     				=						compute_measures(lowercase_      ,		lowercase_  )['''wer''']

              _snake_case      :							Tuple     				=						compute_measures(lowercase_      ,		lowercase_      ,		truth_transform=lowercase_      ,		hypothesis_transform=lowercase_  )['''wer''']
              return {"wer": wer, "cer": cer}
       else:

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

              return {"wer": compute_score(lowercase_      ,		lowercase_      ,		'''wer'''  ), "cer": compute_score(lowercase_      ,		lowercase_      ,		'''cer'''  )}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,_KWARGS_DESCRIPTION				)
class 				A    (datasets.Metric				):



      def    __a (					self					)      ->   Tuple:



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



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=None     ,  lowercase_=None					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						bleu_kwargs if bleu_kwargs is not None else {}
             _snake_case      :							str     				=						wer_kwargs if wer_kwargs is not None else {}

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













from __future__ import annotations

class 				A    :



      def __init__(					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						vertices
             _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             print('''Vertex \t Distance from Source'''					)
             for vertex in range(self.vertices					):
                    print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						1E7
             _snake_case      :							Tuple     				=						0

             # Search not nearest vertex not in the shortest path tree
             for vertex in range(self.vertices					):
                    if distances_from_source[vertex] < minimum and visited[vertex] is False:
                           _snake_case      :							Dict     				=						distances_from_source[vertex]
                           _snake_case      :							int     				=						vertex
             return min_index



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
             _snake_case      :							List[Any]     				=						0
             _snake_case      :							List[str]     				=						[False] * self.vertices

             for _ in range(self.vertices					):
                    _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                    _snake_case      :							List[str]     				=						True

                    # Update dist value of the adjacent vertices
                    # of the picked vertex only if the current
                    # distance is greater than new distance and
                    # the vertex in not in the shortest path tree
                    for v in range(self.vertices					):
                           if (
                               self.graph[u][v] > 0
                               and visited[v] is False
                               and distances[v] > distances[u] + self.graph[u][v]
                           ):
                                  _snake_case      :							Any     				=						distances[u] + self.graph[u][v]

             self.print_solution(lowercase_					)


if __name__ == "__main__":
   lowerCAmelCase_   =   Graph(9)
   lowerCAmelCase_   =   [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
       [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
       [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)






