


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A   =			{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   =			[
        """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

    A   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













'''simple docstring'''



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

import datasets


__UpperCamelCase   :							Optional[int]         =   """\
@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   :							Tuple         =   """\
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   :							Optional[int]         =   """
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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str     )	->      List[Any]:

   """simple docstring"""
   return float((preds == labels).mean()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     simple_accuracy(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     float(fa_score(y_true=SCREAMING_SNAKE_CASE__,       y_pred=SCREAMING_SNAKE_CASE__     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      int:

   """simple docstring"""
   __a					  =     float(pearsonr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   __a					  =     float(spearmanr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[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 			__UpperCamelCase				(	self   )				->int:



         '''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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         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"]'   )