

"""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   _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

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




