






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

import datasets


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

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

__a:     Any   					=					'''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "precision": Precision@10
Examples:

    >>> 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"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'precision@10\': 1.0}

'''




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       return float((preds == labels).mean() )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[int]:
       _UpperCAmelCase							=    simple_accuracy(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    float(fa_score(y_true=__snake_case   ,      y_pred=__snake_case ) )
       return {
           "accuracy": acc,
           "f1": fa,
       }




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Any:
       _UpperCAmelCase							=    np.array(__snake_case )
       _UpperCAmelCase							=    np.array(__snake_case )
       _UpperCAmelCase							=    en_sentvecs.shape[0]

       # mean centering
       _UpperCAmelCase							=    en_sentvecs - np.mean(__snake_case   ,      axis=0 )
       _UpperCAmelCase							=    in_sentvecs - np.mean(__snake_case   ,      axis=0 )

       _UpperCAmelCase							=    cdist(__snake_case   ,      __snake_case   ,      """cosine""" )
       _UpperCAmelCase							=    np.array(range(__snake_case ) )
       _UpperCAmelCase							=    sim.argsort(axis=1 )[:, :1_0]
       _UpperCAmelCase							=    np.any(preds == actual[:, None]   ,      axis=1 )
       return float(matches.mean() )





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

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """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  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[int]							)						->	List[Any]:


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







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]