






'''simple docstring'''

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

import datasets


SCREAMING_SNAKE_CASE__     =       '\\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'

SCREAMING_SNAKE_CASE__     =       '\\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'

SCREAMING_SNAKE_CASE__     =       '\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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      return float((preds == labels).mean()   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Any:
      UpperCamelCase      = simple_accuracy(__UpperCamelCase		,						__UpperCamelCase   )
      UpperCamelCase      = float(fa_score(y_true=__UpperCamelCase		,						y_pred=__UpperCamelCase   )   )
      return {
          "accuracy": acc,
          "f1": fa,
      }







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Optional[Any]:
      UpperCamelCase      = float(pearsonr(__UpperCamelCase		,						__UpperCamelCase   )[0]   )
      UpperCamelCase      = 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    ):

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        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      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'NllbMoeForConditionalGeneration',
         'NllbMoeModel',
         'NllbMoePreTrainedModel',
         'NllbMoeTop2Router',
         'NllbMoeSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

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