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

import datasets


A__           =							"""\
    @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},
}
"""

A__           =							"""\
    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.
"""

A__           =							"""
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					(   a_				: Tuple						,			a_				: List[Any]):
			return float((preds == labels).mean())





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Optional[Any]):
			lowerCamelCase						:int						  =     simple_accuracy(a_						,			a_)
			lowerCamelCase						:Tuple						  =     float(fa_score(y_true=a_						,			y_pred=a_))
			return {
			    "accuracy": acc,
			    "f1": fa,
			}





def  _lowerCamelCase					(   a_				: str						,			a_				: List[str]):
			lowerCamelCase						:Optional[Any]						  =     np.array(a_)
			lowerCamelCase						:Optional[int]						  =     np.array(a_)
			lowerCamelCase						:Any						  =     en_sentvecs.shape[0]

			# mean centering
			lowerCamelCase						:List[str]						  =     en_sentvecs - np.mean(a_						,			axis=0)
			lowerCamelCase						:Tuple						  =     in_sentvecs - np.mean(a_						,			axis=0)

			lowerCamelCase						:int						  =     cdist(a_						,			a_						,			'''cosine''')
			lowerCamelCase						:List[Any]						  =     np.array(range(a_))
			lowerCamelCase						:Any						  =     sim.argsort(axis=1)[:, :10]
			lowerCamelCase						:str						  =     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				):







							def 				snake_case  (				self     :    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 				snake_case  (				self     :    List[Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    str		):
										if self.config_name == "cvit-mkb-clsr":
													return {"precision@10": precision_at_aa(__snake_case	,					__snake_case		)}
										elif self.config_name in ["wiki-ner"]:
													return acc_and_fa(__snake_case	,					__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(__snake_case	,					__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"]'''		)




from typing import TYPE_CHECKING

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


A__           =							{
    """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:
			A__           =							["""LayoutLMv2TokenizerFast"""]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """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

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



