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

import datasets


lowercase   =	'''\
    @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},
}
'''

lowercase   =	'''\
    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.
'''

lowercase   =	'''
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   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   str							)	->     List[Any]:
							return float((preds == labels).mean()							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   Tuple							)	->     Optional[Any]:
							lowerCamelCase_					=       simple_accuracy(UpperCAmelCase__      ,					UpperCAmelCase__							)
							lowerCamelCase_					=       float(fa_score(y_true=UpperCAmelCase__      ,					y_pred=UpperCAmelCase__							)							)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   int							)	->     Dict:
							lowerCamelCase_					=       np.array(UpperCAmelCase__							)
							lowerCamelCase_					=       np.array(UpperCAmelCase__							)
							lowerCamelCase_					=       en_sentvecs.shape[0]

							# mean centering
							lowerCamelCase_					=       en_sentvecs - np.mean(UpperCAmelCase__      ,					axis=0							)
							lowerCamelCase_					=       in_sentvecs - np.mean(UpperCAmelCase__      ,					axis=0							)

							lowerCamelCase_					=       cdist(UpperCAmelCase__      ,					UpperCAmelCase__      ,					"""cosine"""							)
							lowerCamelCase_					=       np.array(range(UpperCAmelCase__							)							)
							lowerCamelCase_					=       sim.argsort(axis=1							)[:, :1_0]
							lowerCamelCase_					=       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							):

					def  lowercase__   (						self       :  Optional[Any] ):
												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       :  List[Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[int] ):
												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\"]""" )







from typing import TYPE_CHECKING

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


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





