





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class      A       (	__lowercase						):
      _snake_case							     =(DPMSolverSDEScheduler,)
      _snake_case							     =10



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  **_lowerCAmelCase:						str       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1100,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "noise_sampler_seed": 0,
         }

         config.update(**_lowerCAmelCase       )
         return config



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

         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



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

         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]		,  [0.00_02, 0.0_02, 0.02]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



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

         for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=_lowerCAmelCase       )



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

         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=_lowerCAmelCase       )



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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase_											=sample.to(_lowerCAmelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase_											=scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75       ) < 1e-2
            assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06       ) < 1e-2
            assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52       ) < 1e-3
         else:
            assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62       ) < 1e-2
            assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1e-3



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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(prediction_type="v_prediction"       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase_											=sample.to(_lowerCAmelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase_											=scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53       ) < 1e-2
            assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03       ) < 1e-2
            assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97       ) < 1e-3
         else:
            assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25       ) < 1e-2
            assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21       ) < 1e-3



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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps		,  device=_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter.to(_lowerCAmelCase       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
            UpperCAmelCase_											=scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38       ) < 1e-2
            assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35       ) < 1e-3
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12       ) < 1e-2
            assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71       ) < 1e-3
         else:
            assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62       ) < 1e-2
            assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26       ) < 1e-3






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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase		,  use_karras_sigmas=_lowerCAmelCase       )

         scheduler.set_timesteps(self.num_inference_steps		,  device=_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter.to(_lowerCAmelCase       ) * scheduler.init_noise_sigma
         UpperCAmelCase_											=sample.to(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=scheduler.scale_model_input(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )

            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=output.prev_sample

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         if torch_device in ["mps"]:
            assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1e-2
         elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 1e-2
         else:
            assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72       ) < 1e-2
            assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11       ) < 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,
         }




