





'''simple docstring'''






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 		lowerCamelCase  (      _A       ):
 snake_case_      =   (DDIMParallelScheduler,)
 snake_case_      =   (("eta", 0.0), ("num_inference_steps", 50))



 def   _lowerCamelCase    (   self		,      **a_	):
   lowerCAmelCase     :    Dict						=   {
       "num_train_timesteps": 1_000,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "clip_sample": True,
   }

   config.update(**a_	)
   return config



 def   _lowerCamelCase    (   self		,      **a_	):
   lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0]
   lowerCAmelCase     :    int						=   self.get_scheduler_config(**a_	)
   lowerCAmelCase     :    List[Any]						=   scheduler_class(**a_	)

   lowerCAmelCase     :    Dict						=   10, 0.0

   lowerCAmelCase     :    Optional[Any]						=   self.dummy_model()
   lowerCAmelCase     :    Any						=   self.dummy_sample_deter

   scheduler.set_timesteps(a_	)

   for t in scheduler.timesteps:
     lowerCAmelCase     :    Any						=   model(a_		,      a_	)
     lowerCAmelCase     :    Tuple						=   scheduler.step(a_		,      a_		,      a_		,      a_	).prev_sample

   return sample



 def   _lowerCamelCase    (   self	):
   for timesteps in [100, 500, 1_000]:
     self.check_over_configs(num_train_timesteps=a_	)



 def   _lowerCamelCase    (   self	):
   for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=a_	)

   lowerCAmelCase     :    Dict						=   self.scheduler_classes[0]
   lowerCAmelCase     :    Optional[Any]						=   self.get_scheduler_config(steps_offset=1	)
   lowerCAmelCase     :    Union[str, Any]						=   scheduler_class(**a_	)
   scheduler.set_timesteps(5	)
   assert torch.equal(scheduler.timesteps		,      torch.LongTensor([801, 601, 401, 201, 1]	)	)



 def   _lowerCamelCase    (   self	):
   for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,      [0.002, 0.02, 0.2, 2]	):
     self.check_over_configs(beta_start=a_		,      beta_end=a_	)



 def   _lowerCamelCase    (   self	):
   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=a_	)



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



 def   _lowerCamelCase    (   self	):
   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=a_	)



 def   _lowerCamelCase    (   self	):
   for timestep_spacing in ["trailing", "leading"]:
     self.check_over_configs(timestep_spacing=a_	)



 def   _lowerCamelCase    (   self	):
   for rescale_betas_zero_snr in [True, False]:
     self.check_over_configs(rescale_betas_zero_snr=a_	)



 def   _lowerCamelCase    (   self	):
   self.check_over_configs(thresholding=a_	)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(
           thresholding=a_		,      prediction_type=a_		,      sample_max_value=a_		,      )



 def   _lowerCamelCase    (   self	):
   for t in [1, 10, 49]:
     self.check_over_forward(time_step=a_	)



 def   _lowerCamelCase    (   self	):
   for t, num_inference_steps in zip([1, 10, 50]		,      [10, 50, 500]	):
     self.check_over_forward(time_step=a_		,      num_inference_steps=a_	)



 def   _lowerCamelCase    (   self	):
   for t, eta in zip([1, 10, 49]		,      [0.0, 0.5, 1.0]	):
     self.check_over_forward(time_step=a_		,      eta=a_	)



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0]
   lowerCAmelCase     :    List[str]						=   self.get_scheduler_config()
   lowerCAmelCase     :    str						=   scheduler_class(**a_	)

   assert torch.sum(torch.abs(scheduler._get_variance(0		,      0	) - 0.0	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(420		,      400	) - 0.14771	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(980		,      960	) - 0.32460	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(0		,      0	) - 0.0	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(487		,      486	) - 0.00979	)	) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(999		,      998	) - 0.02	)	) < 1e-5



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Optional[Any]						=   self.scheduler_classes[0]
   lowerCAmelCase     :    Any						=   self.get_scheduler_config()
   lowerCAmelCase     :    Tuple						=   scheduler_class(**a_	)

   lowerCAmelCase     :    Dict						=   10, 0.0
   scheduler.set_timesteps(a_	)

   lowerCAmelCase     :    int						=   self.dummy_model()
   lowerCAmelCase     :    List[str]						=   self.dummy_sample_deter
   lowerCAmelCase     :    Union[str, Any]						=   self.dummy_sample_deter + 0.1
   lowerCAmelCase     :    Any						=   self.dummy_sample_deter - 0.1

   lowerCAmelCase     :    Optional[int]						=   samplea.shape[0]
   lowerCAmelCase     :    Optional[Any]						=   torch.stack([samplea, samplea, samplea]		,      dim=0	)
   lowerCAmelCase     :    List[Any]						=   torch.arange(a_	)[0:3, None].repeat(1		,      a_	)

   lowerCAmelCase     :    Any						=   model(samples.flatten(0		,      1	)		,      timesteps.flatten(0		,      1	)	)
   lowerCAmelCase     :    int						=   scheduler.batch_step_no_noise(a_		,      timesteps.flatten(0		,      1	)		,      samples.flatten(0		,      1	)		,      a_	)

   lowerCAmelCase     :    List[str]						=   torch.sum(torch.abs(a_	)	)
   lowerCAmelCase     :    List[Any]						=   torch.mean(torch.abs(a_	)	)

   assert abs(result_sum.item() - 1147.7904	) < 1e-2
   assert abs(result_mean.item() - 0.4982	) < 1e-3



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   self.full_loop()

   lowerCAmelCase     :    Optional[Any]						=   torch.sum(torch.abs(a_	)	)
   lowerCAmelCase     :    List[Any]						=   torch.mean(torch.abs(a_	)	)

   assert abs(result_sum.item() - 172.0067	) < 1e-2
   assert abs(result_mean.item() - 0.223967	) < 1e-3



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[Any]						=   self.full_loop(prediction_type="v_prediction"	)

   lowerCAmelCase     :    Dict						=   torch.sum(torch.abs(a_	)	)
   lowerCAmelCase     :    List[str]						=   torch.mean(torch.abs(a_	)	)

   assert abs(result_sum.item() - 52.5302	) < 1e-2
   assert abs(result_mean.item() - 0.0684	) < 1e-3



 def   _lowerCamelCase    (   self	):
   # We specify different beta, so that the first alpha is 0.99
   lowerCAmelCase     :    Any						=   self.full_loop(set_alpha_to_one=a_		,      beta_start=0.01	)
   lowerCAmelCase     :    List[str]						=   torch.sum(torch.abs(a_	)	)
   lowerCAmelCase     :    Optional[int]						=   torch.mean(torch.abs(a_	)	)

   assert abs(result_sum.item() - 149.8295	) < 1e-2
   assert abs(result_mean.item() - 0.1951	) < 1e-3





 def   _lowerCamelCase    (   self	):
   # We specify different beta, so that the first alpha is 0.99
   lowerCAmelCase     :    Any						=   self.full_loop(set_alpha_to_one=a_		,      beta_start=0.01	)
   lowerCAmelCase     :    Tuple						=   torch.sum(torch.abs(a_	)	)
   lowerCAmelCase     :    Dict						=   torch.mean(torch.abs(a_	)	)

   assert abs(result_sum.item() - 149.0784	) < 1e-2
   assert abs(result_mean.item() - 0.1941	) < 1e-3










'''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,
			}



