


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

import datasets


lowercase_			       =      '\\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'

lowercase_			       =      '\\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'

lowercase_			       =      '\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  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 return float((preds == labels).mean()		)
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Optional[Any]						   =		simple_accuracy(UpperCamelCase__     ,			UpperCamelCase__		)
 __lowerCamelCase						:				List[Any]						   =		float(fa_score(y_true=UpperCamelCase__     ,			y_pred=UpperCamelCase__		)		)
 return {
     "accuracy": acc,
     "f1": fa,
 }
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Union[str, Any]						   =		float(pearsonr(UpperCamelCase__     ,			UpperCamelCase__		)[0]		)
 __lowerCamelCase						:				Optional[Any]						   =		float(spearmanr(UpperCamelCase__     ,			UpperCamelCase__		)[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 	_snake_case      (							self:			Dict							):

       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 	_snake_case      (							self:			Dict		, a:			Union[str, Any]		, a:			str							):

       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(lowercase__		, lowercase__							)}
       elif self.config_name == "stsb":
        return pearson_and_spearman(lowercase__		, lowercase__							)
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(lowercase__		, lowercase__							)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(lowercase__		, lowercase__							)}
       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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  List[Any]				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
