


'''simple docstring'''






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

import datasets


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

__UpperCAmelCase       							=				'''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

__UpperCAmelCase       							=				'''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''


def    _snake_case  (	A					,       A			)		->       Union[str, Any]:
							return float((preds == labels).mean()			)


def    _snake_case  (	A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      simple_accuracy(A					,       A			)
							lowerCAmelCase__			  =      float(fa_score(y_true=A					,       y_pred=A			)			)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      float(pearsonr(A					,       A			)[0]			)
							lowerCAmelCase__			  =      float(spearmanr(A					,       A			)[0]			)
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}






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





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														if self.config_name == "cola":
																					return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_   ,       lowerCamelCase_  )}
														elif self.config_name == "stsb":
																					return pearson_and_spearman(lowerCamelCase_   ,       lowerCamelCase_  )
														elif self.config_name in ["mrpc", "qqp"]:
																					return acc_and_fa(lowerCamelCase_   ,       lowerCamelCase_  )
														elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																					return {"accuracy": simple_accuracy(lowerCamelCase_   ,       lowerCamelCase_  )}
														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


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

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