





'''simple docstring'''






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       ):



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[int]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.dummy_uncond_unet
			lowerCAmelCase     :    int						=   KarrasVeScheduler()

			lowerCAmelCase     :    Optional[int]						=   KarrasVePipeline(unet=a_		,      scheduler=a_	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   pipe(num_inference_steps=2		,      generator=a_		,      output_type="numpy"	).images

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    List[Any]						=   pipe(num_inference_steps=2		,      generator=a_		,      output_type="numpy"		,      return_dict=a_	)[0]

			lowerCAmelCase     :    List[Any]						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    Optional[Any]						=   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase     :    str						=   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       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   "google/ncsnpp-celebahq-256"
			lowerCAmelCase     :    List[str]						=   UNetaDModel.from_pretrained(a_	)
			lowerCAmelCase     :    str						=   KarrasVeScheduler()

			lowerCAmelCase     :    List[Any]						=   KarrasVePipeline(unet=a_		,      scheduler=a_	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Tuple						=   torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   pipe(num_inference_steps=20		,      generator=a_		,      output_type="numpy"	).images

			lowerCAmelCase     :    Any						=   image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			lowerCAmelCase     :    int						=   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










'''simple docstring'''






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

import datasets


lowerCAmelCase       		=      """\
@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}
}
"""


lowerCAmelCase       		=      """\
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.
"""


lowerCAmelCase       		=      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 		lowerCamelCase  (      datasets.Metric       ):



	def   _lowerCamelCase    (   self	):
			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		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   0.0
			for i, j in zip(a_		,      a_	):
					n_correct += 1.0 if math_equivalence.is_equiv(a_		,      a_	) else 0.0
			lowerCAmelCase     :    List[str]						=   n_correct / len(a_	)
			return {
			    "accuracy": accuracy,
			}



