






"""simple docstring"""





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

import datasets


UpperCamelCase_	:				Tuple										= """\
    @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},
}
"""

UpperCamelCase_	:				List[str]										= """\
    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.
"""

UpperCamelCase_	:				str										= """
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     __lowercase					(    a :				Tuple       ,		a :				Dict )					->					List[str]:
      return float((preds == labels).mean() )



def     __lowercase					(    a :				str       ,		a :				Tuple )					->					Any:
      __snake_case      :    List[str]  			=simple_accuracy(a       ,		a )
      __snake_case      :    Any  			=float(fa_score(y_true=a       ,		y_pred=a ) )
      return {
          "accuracy": acc,
          "f1": fa,
      }



def     __lowercase					(    a :				Optional[Any]       ,		a :				Any )					->					Optional[int]:
      __snake_case      :    str  			=np.array(a )
      __snake_case      :    Optional[int]  			=np.array(a )
      __snake_case      :    Dict  			=en_sentvecs.shape[0]

      # mean centering
      __snake_case      :    List[Any]  			=en_sentvecs - np.mean(a       ,		axis=0 )
      __snake_case      :    Dict  			=in_sentvecs - np.mean(a       ,		axis=0 )

      __snake_case      :    Dict  			=cdist(a       ,		a       ,		'''cosine''' )
      __snake_case      :    List[str]  			=np.array(range(a ) )
      __snake_case      :    List[str]  			=sim.argsort(axis=1 )[:, :10]
      __snake_case      :    int  			=np.any(preds == actual[:, None]       ,		axis=1 )
      return float(matches.mean() )


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


      def _UpperCamelCase						(    self				:	Dict						):





            """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 _UpperCamelCase						(    self				:	int							,   a				:	Union[str, Any]							,   a				:	Any						):





            """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"]'''						)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_	:				str										= {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Union[str, Any]										= ["""LayoutXLMTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


