"""simple docstring"""






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

import datasets


__A			   = '\\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'

__A			   = '\\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'

__A			   = '\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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  str:
   return float((preds == labels).mean() )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   lowercase__:   str					=      simple_accuracy(a__       ,    a__ )
   lowercase__:   Any					=      float(fa_score(y_true=a__       ,    y_pred=a__ ) )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   lowercase__:   str					=      np.array(a__ )
   lowercase__:   List[str]					=      np.array(a__ )
   lowercase__:   Optional[int]					=      en_sentvecs.shape[0]

   # mean centering
   lowercase__:   Optional[int]					=      en_sentvecs - np.mean(a__       ,    axis=0 )
   lowercase__:   Union[str, Any]					=      in_sentvecs - np.mean(a__       ,    axis=0 )

   lowercase__:   str					=      cdist(a__       ,    a__       ,    '''cosine''' )
   lowercase__:   Tuple					=      np.array(range(a__ ) )
   lowercase__:   int					=      sim.argsort(axis=1 )[:, :1_0]
   lowercase__:   Dict					=      np.any(preds == actual[:, None]       ,    axis=1 )
   return float(matches.mean() )




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



    """simple docstring"""

    def _snake_case			(   self							):




       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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):




       if self.config_name == "cvit-mkb-clsr":
          return {"precision@10": precision_at_aa(__SCREAMING_SNAKE_CASE    ,			__SCREAMING_SNAKE_CASE							)}
       elif self.config_name in ["wiki-ner"]:
          return acc_and_fa(__SCREAMING_SNAKE_CASE    ,			__SCREAMING_SNAKE_CASE							)
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
          return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE    ,			__SCREAMING_SNAKE_CASE							)}
       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\"]'''							)



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       str			   =     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "convnextv2"
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=3					,							__SCREAMING_SNAKE_CASE					:       Dict=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=4					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0.02					,							__SCREAMING_SNAKE_CASE					:       Dict=1E-12					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=224					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_stages
      __SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(self.depths			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






