


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase	: List[str]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Dict  				={
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict						=		'''bert'''


				def __init__(       self			:Tuple			,			lowerCAmelCase__			:str=30_522			,			lowerCAmelCase__			:Tuple=768			,			lowerCAmelCase__			:Dict=12			,			lowerCAmelCase__			:Tuple=12			,			lowerCAmelCase__			:List[str]=3_072			,			lowerCAmelCase__			:Any="gelu"			,			lowerCAmelCase__			:Optional[Any]=0.1			,			lowerCAmelCase__			:List[str]=0.1			,			lowerCAmelCase__			:List[str]=512			,			lowerCAmelCase__			:List[Any]=2			,			lowerCAmelCase__			:int=0.02			,			lowerCAmelCase__			:int=1E-1_2			,			lowerCAmelCase__			:str=0			,			lowerCAmelCase__			:List[str]="absolute"			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Optional[int]=None			,			**lowerCAmelCase__			:List[Any]			,			)		->			Optional[int]:
										super().__init__(pad_token_id=lowerCAmelCase__			,			**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Any					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     int					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		position_embedding_type
										__SCREAMING_SNAKE_CASE						:     int					=		use_cache
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		classifier_dropout




class        _lowercase      (			A__       ):






				'''simple docstring'''



				@property
				def 		__magic_name__(       self			:Any	)		->			Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]	)




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

import datasets


__lowerCAmelCase	: 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'

__lowerCAmelCase	: Optional[Any]  				='\\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'

__lowerCAmelCase	: Dict  				='\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        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return float((preds == labels).mean()      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		simple_accuracy(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		float(fa_score(y_true=lowercase__    ,       y_pred=lowercase__      )      )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		en_sentvecs.shape[0]

						# mean centering
						__SCREAMING_SNAKE_CASE						:     Tuple					=		en_sentvecs - np.mean(lowercase__    ,       axis=0      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		in_sentvecs - np.mean(lowercase__    ,       axis=0      )

						__SCREAMING_SNAKE_CASE						:     str					=		cdist(lowercase__    ,       lowercase__    ,       '''cosine'''      )
						__SCREAMING_SNAKE_CASE						:     int					=		np.array(range(lowercase__      )      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		sim.argsort(axis=1      )[:, :10]
						__SCREAMING_SNAKE_CASE						:     str					=		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       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Tuple	)		->			Tuple:
										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 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Tuple	)		->			str:
										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"]'''	)
