



"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	FunnelTokenizer
	SCREAMING_SNAKE_CASE          =	FunnelTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase      :  str												=       [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase      :  int												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       str:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "UNwant\u00E9d,running"
				__lowerCAmelCase      :  str												=       "unwanted, running"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase      :  Any												=       tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			[7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase      :  Optional[int]												=       len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len)

							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running"       ,			"UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len + [1] * sentence_len)




"""simple docstring"""






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

import datasets


__snake_case							:   Optional[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'

__snake_case							:   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'

__snake_case							:   str      =    '\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 			_lowercase				(  __snake_case					,__snake_case	)						->   Union[str, Any]:
			return float((preds == labels).mean()	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   str:
			__lowerCAmelCase      :  str												=       simple_accuracy(__snake_case					,__snake_case	)
			__lowerCAmelCase      :  Any												=       float(fa_score(y_true=__snake_case					,y_pred=__snake_case	)	)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}


def 			_lowercase				(  __snake_case					,__snake_case	)						->   int:
			__lowerCAmelCase      :  Union[str, Any]												=       np.array(__snake_case	)
			__lowerCAmelCase      :  Tuple												=       np.array(__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       en_sentvecs.shape[0]

			# mean centering
			__lowerCAmelCase      :  Union[str, Any]												=       en_sentvecs - np.mean(__snake_case					,axis=0	)
			__lowerCAmelCase      :  int												=       in_sentvecs - np.mean(__snake_case					,axis=0	)

			__lowerCAmelCase      :  Optional[Any]												=       cdist(__snake_case					,__snake_case					,"cosine"	)
			__lowerCAmelCase      :  int												=       np.array(range(__snake_case	)	)
			__lowerCAmelCase      :  int												=       sim.argsort(axis=1	)[:, :10]
			__lowerCAmelCase      :  Optional[Any]												=       np.any(preds == actual[:, None]					,axis=1	)
			return float(matches.mean()	)





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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       str:

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

				"""simple docstring"""

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