



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

import datasets


__UpperCAmelCase			  =			'''\
@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			  =			'''\
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			  =			'''
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      (  snake_case__			:       Union[str, Any]			,	snake_case__			:       Optional[int]      )      ->	Optional[int]:
   return float((preds == labels).mean()      )
def 						UpperCamelCase      (  snake_case__			:       str			,	snake_case__			:       int      )      ->	str:
   UpperCamelCase       :    str		      =      simple_accuracy(snake_case__			,	snake_case__      )
   UpperCamelCase       :    List[Any]		      =      float(fa_score(y_true=snake_case__			,	y_pred=snake_case__      )      )
   return {
       "accuracy": acc,
       "f1": fa,
   }
def 						UpperCamelCase      (  snake_case__			:       Tuple			,	snake_case__			:       Union[str, Any]      )      ->	Union[str, Any]:
   UpperCamelCase       :    List[Any]		      =      float(pearsonr(snake_case__			,	snake_case__      )[0]      )
   UpperCamelCase       :    Optional[Any]		      =      float(spearmanr(snake_case__			,	snake_case__      )[0]      )
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION				)
class 							lowerCAmelCase_						(    datasets.Metric				):
       def    snake_case_			(     self			)	->						int:
          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,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          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'''




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

