

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	):



       """simple docstring"""







       def __init__( self	:				str     ,snake_case	:				str = " "							):
            SCREAMING_SNAKE_CASE 		=sentence_delimiter
       def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				str							):
            return list(snake_case							)


       def 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				List[str]							):
            SCREAMING_SNAKE_CASE 		=[]
            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







 _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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_="exp",       lowerCAmelCase_=None,       lowerCAmelCase_=False,       lowerCAmelCase_=False,       lowerCAmelCase_=None,       lowerCAmelCase_=False,       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[[label] for label in labels]
     SCREAMING_SNAKE_CASE 		=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"`.'     )

     SCREAMING_SNAKE_CASE 		=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'     )
     SCREAMING_SNAKE_CASE 		=[[refs[i] for refs in labels] for i in range(lowerCAmelCase_     )]
     SCREAMING_SNAKE_CASE 		=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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """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:
          SCREAMING_SNAKE_CASE 		=compute_measures(lowerCAmelCase_,       lowerCAmelCase_     )['wer']

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

          def compute_score(lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_="wer"     ):
               SCREAMING_SNAKE_CASE 		=0
               SCREAMING_SNAKE_CASE 		=0
               for prediction, reference in zip(lowerCAmelCase_,       lowerCAmelCase_     ):
                    if score_type == "wer":
                         SCREAMING_SNAKE_CASE 		=compute_measures(lowerCAmelCase_,       lowerCAmelCase_     )
                    elif score_type == "cer":
                         SCREAMING_SNAKE_CASE 		=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 						a_			(				datasets.Metric	):



      """simple docstring"""







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

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




def      snake_case__							(  lowerCAmelCase_ = 100     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[[0 for _ in range(lowerCAmelCase_     )] for _ in range(m + 1     )]
     for i in range(m + 1     ):
          SCREAMING_SNAKE_CASE 		=1

     for n in range(m + 1     ):
          for k in range(1,       lowerCAmelCase_     ):
               memo[n][k] += memo[n][k - 1]
               if n > k:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1] - 1


if __name__ == "__main__":
 print(solution(int(input("Enter a number: ").strip())))

