
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_A   =     "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

_A   =     "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

_A   =     "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Dict				):




			"""simple docstring"""


			return float((preds == labels).mean()				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	simple_accuracy(__lowerCAmelCase    ,							__lowerCAmelCase				)
			lowerCAmelCase_						=	float(fa_score(y_true=__lowerCAmelCase    ,							y_pred=__lowerCAmelCase				)				)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			lowerCAmelCase_						=	float(pearsonr(__lowerCAmelCase    ,							__lowerCAmelCase				)[0]				)
			lowerCAmelCase_						=	float(spearmanr(__lowerCAmelCase    ,							__lowerCAmelCase				)[0]				)
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			}





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




				def      __a						(					self     )				->      Tuple:
							if self.config_name not in [
							    "sst2",
							    "mnli",
							    "mnli_mismatched",
							    "mnli_matched",
							    "cola",
							    "stsb",
							    "mrpc",
							    "qqp",
							    "qnli",
							    "rte",
							    "wnli",
							    "hans",
							]:
										raise KeyError(
										    "You should supply a configuration name selected in "
										    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
										    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"     )
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
							        {
							            "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"     ),
							            "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"     ),
							        }     )				,				codebase_urls=[]				,				reference_urls=[]				,				format="numpy"				,				)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[Any]:
							if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(_UpperCamelCase				,				_UpperCamelCase     )}
							elif self.config_name == "stsb":
										return pearson_and_spearman(_UpperCamelCase				,				_UpperCamelCase     )
							elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(_UpperCamelCase				,				_UpperCamelCase     )
							elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(_UpperCamelCase				,				_UpperCamelCase     )}
							else:
										raise KeyError(
										    "You should supply a configuration name selected in "
										    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
										    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"     )





from typing import TYPE_CHECKING

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


_A   =     {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

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



