

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			UNetaDModel
      _lowerCamelCase       :			KarrasVeScheduler





      def __init__(   self							:				Union[str, Any]						,			UpperCAmelCase							:				UNetaDModel						,			UpperCAmelCase							:				KarrasVeScheduler	):
            super().__init__()
            self.register_modules(unet=UpperCAmelCase						,			scheduler=UpperCAmelCase	)







      @torch.no_grad()
      def __call__(   self							:				Union[str, Any]						,			UpperCAmelCase							:				int = 1						,			UpperCAmelCase							:				int = 50						,			UpperCAmelCase							:				Optional[Union[torch.Generator, List[torch.Generator]]] = None						,			UpperCAmelCase							:				Optional[str] = "pil"						,			UpperCAmelCase							:				bool = True						,			**UpperCAmelCase							:				Tuple						,			):
            A_						  =				self.unet.config.sample_size
            A_						  =				(batch_size, 3, img_size, img_size)

            A_						  =				self.unet

            # sample x_0 ~ N(0, sigma_0^2 * I)
            A_						  =				randn_tensor(UpperCAmelCase						,			generator=UpperCAmelCase						,			device=self.device	) * self.scheduler.init_noise_sigma

            self.scheduler.set_timesteps(UpperCAmelCase	)

            for t in self.progress_bar(self.scheduler.timesteps	):
                  # here sigma_t == t_i from the paper
                  A_						  =				self.scheduler.schedule[t]
                  A_						  =				self.scheduler.schedule[t - 1] if t > 0 else 0

                  # 1. Select temporarily increased noise level sigma_hat
                  # 2. Add new noise to move from sample_i to sample_hat
                  A_  ,     A_						  =				self.scheduler.add_noise_to_input(UpperCAmelCase						,			UpperCAmelCase						,			generator=UpperCAmelCase	)

                  # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                  # The model inputs and output are adjusted by following eq. (213) in [1].
                  A_						  =				(sigma_hat / 2) * model((sample_hat + 1) / 2						,			sigma_hat / 2	).sample

                  # 4. Evaluate dx/dt at sigma_hat
                  # 5. Take Euler step from sigma to sigma_prev
                  A_						  =				self.scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)

                  if sigma_prev != 0:
                        # 6. Apply 2nd order correction
                        # The model inputs and output are adjusted by following eq. (213) in [1].
                        A_						  =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,			sigma_prev / 2	).sample
                        A_						  =				self.scheduler.step_correct(
                            UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			step_output.prev_sample						,			step_output["derivative"]						,			)
                  A_						  =				step_output.prev_sample

            A_						  =				(sample / 2 + 0.5).clamp(0						,			1	)
            A_						  =				sample.cpu().permute(0						,			2						,			3						,			1	).numpy()
            if output_type == "pil":
                  A_						  =				self.numpy_to_pil(UpperCAmelCase	)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=UpperCAmelCase	)

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     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





