




from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


UpperCamelCase__    			=			datasets.utils.logging.get_logger(__name__)
class 			a__    (					folder_based_builder.FolderBasedBuilderConfig      ):
						_a							:    bool					=    None
						_a							:    bool					=    None






class 			a__    (					folder_based_builder.FolderBasedBuilder      ):
						_a							:    int					=    datasets.Audio()
						_a							:    Optional[Any]					=    """audio"""
						_a							:    str					=    AudioFolderConfig
						_a							:    List[str]  # definition at the bottom of the script
						_a							:    Union[str, Any]					=    AudioClassification(audio_column="""audio""" ,				label_column="""label"""      )






UpperCamelCase__    			=			[
    """.aiff""",
    """.au""",
    """.avr""",
    """.caf""",
    """.flac""",
    """.htk""",
    """.svx""",
    """.mat4""",
    """.mat5""",
    """.mpc2k""",
    """.ogg""",
    """.paf""",
    """.pvf""",
    """.raw""",
    """.rf64""",
    """.sd2""",
    """.sds""",
    """.ircam""",
    """.voc""",
    """.w64""",
    """.wav""",
    """.nist""",
    """.wavex""",
    """.wve""",
    """.xi""",
    """.mp3""",
    """.opus""",
]
UpperCamelCase__    			=			AUDIO_EXTENSIONS







'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


lowerCamelCase :      List[Any]					 =     "\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

lowerCamelCase :      Optional[Any]					 =     "\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

lowerCamelCase :      int					 =     "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Tuple				)      ->			List[Any]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Any				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =simple_accuracy(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =float(fa_score(y_true=_UpperCamelCase							, y_pred=_UpperCamelCase				)				)
     return {
         "accuracy": acc,
         "f1": fa,
     }


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any							, _UpperCamelCase						:     int				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =en_sentvecs.shape[0]

     # mean centering
     _SCREAMING_SNAKE_CASE							    =en_sentvecs - np.mean(_UpperCamelCase							, axis=0				)
     _SCREAMING_SNAKE_CASE							    =in_sentvecs - np.mean(_UpperCamelCase							, axis=0				)

     _SCREAMING_SNAKE_CASE							    =cdist(_UpperCamelCase							, _UpperCamelCase							, 'cosine'				)
     _SCREAMING_SNAKE_CASE							    =np.array(range(_UpperCamelCase				)				)
     _SCREAMING_SNAKE_CASE							    =sim.argsort(axis=1				)[:, :10]
     _SCREAMING_SNAKE_CASE							    =np.any(preds == actual[:, None]							, axis=1				)
     return float(matches.mean()				)







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





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       if self.config_name not in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "cvit-mkb-clsr",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
           "wiki-ner",
       ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                '"wiki-ner"]' )
       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' )
                   if self.config_name != 'cvit-mkb-clsr'
                   else datasets.Sequence(datasets.Value('float32' ) ),
                   'references': datasets.Value('int64' )
                   if self.config_name != 'cvit-mkb-clsr'
                   else datasets.Sequence(datasets.Value('float32' ) ),
               } )							,					codebase_urls=[]							,					reference_urls=[]							,					format='numpy' if self.config_name != 'cvit-mkb-clsr' else None							,					)





  def 						A  (		self      :	List[str]							,					_a      :	Tuple							,					_a      :	Optional[int] )	->    int:





       '''simple docstring'''






       if self.config_name == "cvit-mkb-clsr":
            return {"precision@10": precision_at_aa(_a							,					_a )}
       elif self.config_name in ["wiki-ner"]:
            return acc_and_fa(_a							,					_a )
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
            return {"accuracy": simple_accuracy(_a							,					_a )}
       else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
                '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
                '"wiki-ner"]' )
