




'''simple docstring'''



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

import datasets


__UpperCamelCase      :       List[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},
}
"""

__UpperCamelCase      :       Tuple      			=      """\
    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      :       Dict      			=      """
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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Union[str, Any]:

   """simple docstring"""
   return float((preds == labels).mean()    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    simple_accuracy(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   __a				     =    float(fa_score(y_true=SCREAMING_SNAKE_CASE__,					y_pred=SCREAMING_SNAKE_CASE__    )    )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    )
   __a				     =    en_sentvecs.shape[0]

   # mean centering
   __a				     =    en_sentvecs - np.mean(SCREAMING_SNAKE_CASE__,					axis=0    )
   __a				     =    in_sentvecs - np.mean(SCREAMING_SNAKE_CASE__,					axis=0    )

   __a				     =    cdist(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					'cosine'    )
   __a				     =    np.array(range(SCREAMING_SNAKE_CASE__    )    )
   __a				     =    sim.argsort(axis=1    )[:, :10]
   __a				     =    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     ):




      def  __UpperCamelCase							(  self      )							->List[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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''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"]'      )







'''simple docstring'''



import datasets

from .evaluate import evaluate


__UpperCamelCase      :       int      			=      """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

__UpperCamelCase      :       int      			=      """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

__UpperCamelCase      :       str      			=      """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,      _KWARGS_DESCRIPTION     )
class 							__SCREAMING_SNAKE_CASE       (							datasets.Metric     ):




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return datasets.MetricInfo(
             description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
                 {
                     'predictions': {
                         'id': datasets.Value('string'      ),
                         'prediction_text': datasets.features.Sequence(datasets.Value('string'      )      ),
                     },
                     'references': {
                         'id': datasets.Value('string'      ),
                         'answers': datasets.features.Sequence(
                             {
                                 'text': datasets.Value('string'      ),
                                 'answer_start': datasets.Value('int32'      ),
                             }      ),
                     },
                 }      ) ,   codebase_urls=['https://www.atticusprojectai.org/cuad'] ,   reference_urls=['https://www.atticusprojectai.org/cuad'] ,   )






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



         '''simple docstring'''
         __a				     =    {prediction['id']: prediction['prediction_text'] for prediction in predictions}
         __a				     =    [
             {
                 'paragraphs': [
                     {
                         'qas': [
                             {
                                 'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                 'id': ref['id'],
                             }
                             for ref in references
                         ]
                     }
                 ]
             }
         ]
         __a				     =    evaluate(dataset=lowerCamelCase ,   predictions=lowerCamelCase      )
         return score