






'''simple docstring'''
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        _a(     UpperCamelCase__ :  List[str],		UpperCamelCase__ :  Any							):




      '''simple docstring'''






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





def        _a(     UpperCamelCase__ :  Any,		UpperCamelCase__ :  Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Optional[Any]													=simple_accuracy(lowerCAmelCase_,		lowerCAmelCase_							)
      SCREAMING_SNAKE_CASE__   : Union[str, Any]													=float(fa_score(y_true=lowerCAmelCase_,		y_pred=lowerCAmelCase_							)							)
      return {
          "accuracy": acc,
          "f1": fa,
      }





def        _a(     UpperCamelCase__ :  Dict,		UpperCamelCase__ :  str							):




      '''simple docstring'''






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







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




 def    __magic_name__		(					self		:							str					)		->  List[Any]:
       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    __magic_name__		(					self		:							int    ,					__lowercase		:							List[str]    ,					__lowercase		:							List[Any]					)		->  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"]'''					)




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

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




