


'''simple docstring'''






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    _snake_case  (	A					,       A			)		->       Union[str, Any]:
							return float((preds == labels).mean()			)


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


def    _snake_case  (	A					,       A			)		->       int:
							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 						a__							(				datasets.Metric    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														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   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														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"]'''  )

import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=JukeboxTokenizer
 SCREAMING_SNAKE_CASE_				:					str       						={
     "artist": "Zac Brown Band",
     "genres": "Country",
     "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
 }

 @require_torch
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
              76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
              44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
              47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
              76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
              30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
              27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
              45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
              41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
              76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
              76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
              64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
              30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
              27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
              34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
              27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
              41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
              44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
              76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
              32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
              40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
              45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
              31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
              45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
              34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
              31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
              40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
              38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
              76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
              41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
              27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
              46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
              41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
              46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
              41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
              40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
              27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
              76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
              41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
              76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
              27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
              34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
              44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
              40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
              46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
              38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
              40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
              27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
              76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
              76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
              76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
              76, 76]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )






 @require_torch
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
              31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
              31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
              40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
              79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
              77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
              27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
              37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
              32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
              77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
              77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
              46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
              77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
              77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
              77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
              77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
              64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
              40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
              40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
              38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
              31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
              41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
              46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
              41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
              31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
              31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
              44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
              31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
              38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
              40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
              27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
              31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
              34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
              31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
              31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
              45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
              31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
              15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
              11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
              45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
              41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
              44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
              46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
              27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
              35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
              77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
              31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
              41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
              77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
              40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
              77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
              27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
              77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
              77, 77, 77, 77, 77, 77]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )


