"""simple docstring"""




from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      int:
  return DownloadCommand(args.model			,args.cache_dir			,args.force			,args.trust_remote_code	)






class 						__A							( SCREAMING_SNAKE_CASE_		):






     @staticmethod
     def     __A  (		a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     parser.add_parser("""download"""  )
       download_parser.add_argument(
           """--cache-dir"""				,     type=a__				,     default=a__				,     help="""Path to location to store the models"""  )
       download_parser.add_argument(
           """--force"""				,     action="""store_true"""				,     help="""Force the model to be download even if already in cache-dir"""  )
       download_parser.add_argument(
           """--trust-remote-code"""				,     action="""store_true"""				,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""				,     )
       download_parser.add_argument("""model"""				,     type=a__				,     help="""Name of the model to download"""  )
       download_parser.set_defaults(func=a__  )






     def __init__(		self				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     model
       _lowerCAmelCase						:       List[Any]  			=     cache
       _lowerCAmelCase						:       Tuple  			=     force
       _lowerCAmelCase						:       List[Any]  			=     trust_remote_code






     def     __A  (		self  ):
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model				,     cache_dir=self._cache				,     force_download=self._force				,     trust_remote_code=self._trust_remote_code  )
       AutoTokenizer.from_pretrained(
           self._model				,     cache_dir=self._cache				,     force_download=self._force				,     trust_remote_code=self._trust_remote_code  )






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

import datasets


lowercase  :					str						    =							"""\
@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}
}
"""

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

lowercase  :					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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      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 __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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