





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      A       (	unittest.TestCase						):



    def 	lowerCAmelCase__    (	self:						str       )						->    Any:
       '''simple docstring'''

       super().tearDown()
       gc.collect()



    def 	lowerCAmelCase__    (	self:						int       )						->    Optional[int]:
       '''simple docstring'''

       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
           "stabilityai/stable-diffusion-2"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  )

       UpperCAmelCase_											='''A painting of a squirrel eating a burger'''
       UpperCAmelCase_											=jax.device_count()
       UpperCAmelCase_											=num_samples * [prompt]
       UpperCAmelCase_											=sd_pipe.prepare_inputs(lowercase__       )

       UpperCAmelCase_											=replicate(lowercase__       )
       UpperCAmelCase_											=shard(lowercase__       )

       UpperCAmelCase_											=jax.random.PRNGKey(0       )
       UpperCAmelCase_											=jax.random.split(lowercase__		,  jax.device_count()       )

       UpperCAmelCase_											=sd_pipe(lowercase__		,  lowercase__		,  lowercase__		,  num_inference_steps=25		,  jit=lowercase__       )[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

       UpperCAmelCase_											=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
       UpperCAmelCase_											=images[0, 253:256, 253:256, -1]

       UpperCAmelCase_											=jnp.asarray(jax.device_get(image_slice.flatten()       )       )
       UpperCAmelCase_											=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:						str       )						->    List[str]:
       '''simple docstring'''

       UpperCAmelCase_											='''stabilityai/stable-diffusion-2'''
       UpperCAmelCase_											=FlaxDPMSolverMultistepScheduler.from_pretrained(lowercase__		,  subfolder="scheduler"       )
       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
           lowercase__		,  scheduler=lowercase__		,  revision="bf16"		,  dtype=jnp.bfloataa		,  )
       UpperCAmelCase_											=scheduler_params

       UpperCAmelCase_											='''A painting of a squirrel eating a burger'''
       UpperCAmelCase_											=jax.device_count()
       UpperCAmelCase_											=num_samples * [prompt]
       UpperCAmelCase_											=sd_pipe.prepare_inputs(lowercase__       )

       UpperCAmelCase_											=replicate(lowercase__       )
       UpperCAmelCase_											=shard(lowercase__       )

       UpperCAmelCase_											=jax.random.PRNGKey(0       )
       UpperCAmelCase_											=jax.random.split(lowercase__		,  jax.device_count()       )

       UpperCAmelCase_											=sd_pipe(lowercase__		,  lowercase__		,  lowercase__		,  num_inference_steps=25		,  jit=lowercase__       )[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

       UpperCAmelCase_											=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
       UpperCAmelCase_											=images[0, 253:256, 253:256, -1]

       UpperCAmelCase_											=jnp.asarray(jax.device_get(image_slice.flatten()       )       )
       UpperCAmelCase_											=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











import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowercase  :   int						      ="""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


__lowercase  :   List[Any]						      ="""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowercase  :   str						      =R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""

@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION						)
class      A       (	datasets.Metric						):



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         return datasets.MetricInfo(
             description=_DESCRIPTION		,  citation=_CITATION		,  inputs_description=_KWARGS_DESCRIPTION		,  features=datasets.Features(
                 {
                     "predictions": datasets.Value("string"       ),
                     "references": datasets.Value("string"       ),
                 }       )		,  homepage="https://github.com/hendrycks/math"		,  codebase_urls=["https://github.com/hendrycks/math"]		,  )






      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=0.0
         for i, j in zip(_lowerCAmelCase		,  _lowerCAmelCase       ):
            n_correct += 1.0 if math_equivalence.is_equiv(_lowerCAmelCase		,  _lowerCAmelCase       ) else 0.0
         UpperCAmelCase_											=n_correct / len(_lowerCAmelCase       )
         return {
             "accuracy": accuracy,
         }




