

"""simple docstring"""





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

import datasets


_a :    int							=							'\\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 :    str							=							'\\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 :    List[Any]							=							'\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   _A  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	int)     ->						str:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Any)     ->						List[str]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	int)     ->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						np.array(UpperCamelCase_)
   __lowercase								=						np.array(UpperCamelCase_)
   __lowercase								=						en_sentvecs.shape[0]

   # mean centering
   __lowercase								=						en_sentvecs - np.mean(UpperCamelCase_,      axis=0)
   __lowercase								=						in_sentvecs - np.mean(UpperCamelCase_,      axis=0)

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




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







 """simple docstring"""







 def  _lowercase			(      self							:      List[str]     ):
    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  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      str, UpperCAmelCase__							:      Tuple     ):
    if self.config_name == "cvit-mkb-clsr":
       return {"precision@10": precision_at_aa(UpperCAmelCase__, UpperCAmelCase__     )}
    elif self.config_name in ["wiki-ner"]:
       return acc_and_fa(UpperCAmelCase__, UpperCAmelCase__     )
    elif self.config_name in [
        "wnli",
        "copa",
        "sna",
        "csqa",
        "wstp",
        "inltkh",
        "bbca",
        "iitp-mr",
        "iitp-pr",
        "actsa-sc",
        "md",
    ]:
       return {"accuracy": simple_accuracy(UpperCAmelCase__, UpperCAmelCase__     )}
    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 __future__ import annotations

from typing import Any




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						num_of_nodes
    __lowercase								=						[]
    __lowercase								=						{}


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    self.m_edges.append([u_node, v_node, weight]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] == u_node:
       return u_node
    return self.find_component(self.m_component[u_node]     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):

    if self.m_component[u_node] != u_node:
       for k in self.m_component:
          __lowercase								=						self.find_component(UpperCAmelCase__     )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      list[int], UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):

    if component_size[u_node] <= component_size[v_node]:
       __lowercase								=						v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(UpperCAmelCase__     )

    elif component_size[u_node] >= component_size[v_node]:
       __lowercase								=						self.find_component(UpperCAmelCase__     )
       component_size[u_node] += component_size[v_node]
       self.set_component(UpperCAmelCase__     )



 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						[]
    __lowercase								=						0

    __lowercase								=						[-1] * self.m_num_of_nodes

    # A list of components (initialized to all of the nodes)
    for node in range(self.m_num_of_nodes     ):
       self.m_component.update({node: node}     )
       component_size.append(1     )

    __lowercase								=						self.m_num_of_nodes

    while num_of_components > 1:
       for edge in self.m_edges:
          __lowercase		,__lowercase		,__lowercase								=						edge

          __lowercase								=						self.m_component[u]
          __lowercase								=						self.m_component[v]

          if u_component != v_component:

             for component in (u_component, v_component):
                if (
                    minimum_weight_edge[component] == -1
                    or minimum_weight_edge[component][2] > w
                ):
                   __lowercase								=						[u, v, w]

       for edge in minimum_weight_edge:
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase		,__lowercase		,__lowercase								=						edge

             __lowercase								=						self.m_component[u]
             __lowercase								=						self.m_component[v]

             if u_component != v_component:
                mst_weight += w
                self.union(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
                print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""     )
                num_of_components -= 1

       __lowercase								=						[-1] * self.m_num_of_nodes
    print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""     )







def   _A  (  )     ->						None:
   '''simple docstring'''




if __name__ == "__main__":
   import doctest

   doctest.testmod()




