


"""simple docstring"""






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

import datasets


__snake_case		=       '''\
@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}
}
'''

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

__snake_case		=       '''
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       A_   (   _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     return float((preds == labels).mean()       )
def       A_   (   _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a    	=				simple_accuracy(_lowerCAmelCase,     _lowerCAmelCase       )
     _a    	=				float(fa_score(y_true=_lowerCAmelCase,     y_pred=_lowerCAmelCase       )       )
     return {
         "accuracy": acc,
         "f1": fa,
     }
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				float(pearsonr(_lowerCAmelCase,     _lowerCAmelCase       )[0]       )
     _a    	=				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				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Union[str, 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, is_vision_available


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

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