



'''simple docstring'''





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

import datasets


a_						:     Optional[Any]	     =		'\\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[int]	     =		'\\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_						:     Optional[int]	     =		'\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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		simple_accuracy(_lowerCamelCase   ,   _lowerCamelCase )
      lowerCamelCase								=		float(fa_score(y_true=_lowerCamelCase   ,   y_pred=_lowerCamelCase ) )
      return {
          "accuracy": acc,
          "f1": fa,
      }

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		float(pearsonr(_lowerCamelCase   ,   _lowerCamelCase )[0] )
      lowerCamelCase								=		float(spearmanr(_lowerCamelCase   ,   _lowerCamelCase )[0] )
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION)
class    lowerCamelCase__  (						datasets.Metric):
     """simple docstring"""



     def 						_a      (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 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



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

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2