


'''simple docstring'''






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

import datasets


lowercase				='\\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'

lowercase				='\\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'

lowercase				='\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 lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   return float((preds == labels).mean()				)





def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =simple_accuracy(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =float(fa_score(y_true=__lowerCamelCase			,       y_pred=__lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =float(pearsonr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   _UpperCAmelCase  :  int				       =float(spearmanr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }






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






       def      lowerCAmelCase						(     self)		->    str:




          '''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      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    int:




          '''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"]')





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class    __magic_name__				(						unittest.TestCase							):






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              # Removed: 'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          self.assertTrue(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =[
              'safety_checker/pytorch_model.bin',
              'safety_checker/model.safetensors',
              'vae/diffusion_pytorch_model.bin',
              'vae/diffusion_pytorch_model.safetensors',
              'text_encoder/pytorch_model.bin',
              # Removed: 'text_encoder/model.safetensors',
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          self.assertFalse(is_safetensors_compatible(snake_case))






       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  str				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =[
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  int				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'unet/diffusion_pytorch_model.bin',
              'unet/diffusion_pytorch_model.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  List[str]				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =[
              'text_encoder/pytorch_model.fp16.bin',
              'text_encoder/model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Optional[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          # pass variant but use the non-variant filenames
          _UpperCAmelCase  :  Optional[int]				       =[
              'text_encoder/pytorch_model.bin',
              'text_encoder/model.safetensors',
          ]
          _UpperCAmelCase  :  List[Any]				       ='fp16'
          self.assertTrue(is_safetensors_compatible(snake_case    ,				variant=snake_case))






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =[
              'safety_checker/pytorch_model.fp16.bin',
              'safety_checker/model.fp16.safetensors',
              'vae/diffusion_pytorch_model.fp16.bin',
              'vae/diffusion_pytorch_model.fp16.safetensors',
              'text_encoder/pytorch_model.fp16.bin',
              # 'text_encoder/model.fp16.safetensors',
              'unet/diffusion_pytorch_model.fp16.bin',
              'unet/diffusion_pytorch_model.fp16.safetensors',
          ]
          _UpperCAmelCase  :  Tuple				       ='fp16'
          self.assertFalse(is_safetensors_compatible(snake_case    ,				variant=snake_case))

