


'''simple docstring'''

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

import datasets


a_    :							int         =					"""\\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"""

a_    :							Optional[Any]         =					"""\\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"""

a_    :							str         =					"""\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 __snake_case   ( UpperCAmelCase_		:		Optional[int]   ,							UpperCAmelCase_		:		Dict						):
 return float((preds == labels).mean()						)





def __snake_case   ( UpperCAmelCase_		:		Any   ,							UpperCAmelCase_		:		int						):
 lowerCamelCase_						=    simple_accuracy(A__   ,							A__						)
 lowerCamelCase_						=    float(fa_score(y_true=A__   ,							y_pred=A__						)						)
 return {
     "accuracy": acc,
     "f1": fa,
 }





def __snake_case   ( UpperCAmelCase_		:		Any   ,							UpperCAmelCase_		:		List[Any]						):
 lowerCamelCase_						=    float(pearsonr(A__   ,							A__						)[0]						)
 lowerCamelCase_						=    float(spearmanr(A__   ,							A__						)[0]						)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,		_KWARGS_DESCRIPTION     )
class 	snake_case       (       datasets.Metric     ):





  """simple docstring"""




  def     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     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""






   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\"]"	)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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


