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

import datasets


lowerCamelCase_    :		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},
}
"""

lowerCamelCase_    :		Optional[int]							=	"""\
    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.
"""

lowerCamelCase_    :		str							=	"""
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(     __lowerCamelCase				,    __lowerCamelCase						):
					return float((preds == labels).mean()						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       simple_accuracy(__lowerCamelCase				,    __lowerCamelCase						)
					__a		 =       float(fa_score(y_true=__lowerCamelCase				,    y_pred=__lowerCamelCase						)						)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       np.array(__lowerCamelCase						)
					__a		 =       np.array(__lowerCamelCase						)
					__a		 =       en_sentvecs.shape[0]

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

					__a		 =       cdist(__lowerCamelCase				,    __lowerCamelCase				,    'cosine'						)
					__a		 =       np.array(range(__lowerCamelCase						)						)
					__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 						a__							(       datasets.Metric		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								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				,						UpperCAmelCase				,						UpperCAmelCase   )			->   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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




