


'''simple docstring'''






import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard






@slow
@require_flax
class    __magic_name__				(						unittest.TestCase							):






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




          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()






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




          '''simple docstring'''
          _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =FlaxStableDiffusionPipeline.from_pretrained(
              'stabilityai/stable-diffusion-2'    ,				revision='bf16'    ,				dtype=jnp.bfloataa    ,				)

          _UpperCAmelCase  :  str				       ='A painting of a squirrel eating a burger'
          _UpperCAmelCase  :  int				       =jax.device_count()
          _UpperCAmelCase  :  List[Any]				       =num_samples * [prompt]
          _UpperCAmelCase  :  Tuple				       =sd_pipe.prepare_inputs(snake_case)

          _UpperCAmelCase  :  Any				       =replicate(snake_case)
          _UpperCAmelCase  :  List[Any]				       =shard(snake_case)

          _UpperCAmelCase  :  Any				       =jax.random.PRNGKey(0)
          _UpperCAmelCase  :  Tuple				       =jax.random.split(snake_case    ,				jax.device_count())

          _UpperCAmelCase  :  int				       =sd_pipe(snake_case    ,				snake_case    ,				snake_case    ,				num_inference_steps=2_5    ,				jit=snake_case)[0]
          assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

          _UpperCAmelCase  :  Any				       =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
          _UpperCAmelCase  :  Any				       =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          _UpperCAmelCase  :  Dict				       =jnp.asarray(jax.device_get(image_slice.flatten()))
          _UpperCAmelCase  :  str				       =jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12])
          print(f"output_slice: {output_slice}")
          assert jnp.abs(output_slice - expected_slice).max() < 1E-2






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




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='stabilityai/stable-diffusion-2'
          _UpperCAmelCase       ,			_UpperCAmelCase  :  List[str]				       =FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case    ,				subfolder='scheduler')
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =FlaxStableDiffusionPipeline.from_pretrained(
              snake_case    ,				scheduler=snake_case    ,				revision='bf16'    ,				dtype=jnp.bfloataa    ,				)
          _UpperCAmelCase  :  int				       =scheduler_params

          _UpperCAmelCase  :  Optional[Any]				       ='A painting of a squirrel eating a burger'
          _UpperCAmelCase  :  Tuple				       =jax.device_count()
          _UpperCAmelCase  :  str				       =num_samples * [prompt]
          _UpperCAmelCase  :  int				       =sd_pipe.prepare_inputs(snake_case)

          _UpperCAmelCase  :  int				       =replicate(snake_case)
          _UpperCAmelCase  :  Optional[int]				       =shard(snake_case)

          _UpperCAmelCase  :  Dict				       =jax.random.PRNGKey(0)
          _UpperCAmelCase  :  Tuple				       =jax.random.split(snake_case    ,				jax.device_count())

          _UpperCAmelCase  :  Any				       =sd_pipe(snake_case    ,				snake_case    ,				snake_case    ,				num_inference_steps=2_5    ,				jit=snake_case)[0]
          assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

          _UpperCAmelCase  :  Any				       =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
          _UpperCAmelCase  :  Union[str, Any]				       =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

          _UpperCAmelCase  :  List[str]				       =jnp.asarray(jax.device_get(image_slice.flatten()))
          _UpperCAmelCase  :  Dict				       =jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97])
          print(f"output_slice: {output_slice}")
          assert jnp.abs(output_slice - expected_slice).max() < 1E-2





'''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"]')

