






"""simple docstring"""
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	if isinstance(snake_case__ , torch.Tensor       ):
		return image
	elif isinstance(snake_case__ , PIL.Image.Image       ):
		A_		:    Tuple						     = [image]

	if isinstance(image[0] , PIL.Image.Image       ):
		A_		:    Optional[Any]						     = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""]       )       )[None, :] for i in image]
		A_		:    Union[str, Any]						     = np.concatenate(snake_case__ , axis=0       )
		A_		:    int						     = np.array(snake_case__       ).astype(np.floataa       ) / 255.0
		A_		:    int						     = image.transpose(0 , 3 , 1 , 2       )
		A_		:    Optional[int]						     = 2.0 * image - 1.0
		A_		:    Tuple						     = torch.from_numpy(snake_case__       )
	elif isinstance(image[0] , torch.Tensor       ):
		A_		:    List[str]						     = torch.cat(snake_case__ , dim=0       )
	return image






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__=0.9_995       ):
	if not isinstance(snake_case__ , np.ndarray       ):
		A_		:    Optional[int]						     = True
		A_		:    Dict						     = va.device
		A_		:    Optional[int]						     = va.cpu().numpy()
		A_		:    Tuple						     = va.cpu().numpy()

	A_		:    Dict						     = np.sum(va * va / (np.linalg.norm(snake_case__       ) * np.linalg.norm(snake_case__       ))       )
	if np.abs(snake_case__       ) > DOT_THRESHOLD:
		A_		:    Optional[int]						     = (1 - t) * va + t * va
	else:
		A_		:    Optional[Any]						     = np.arccos(snake_case__       )
		A_		:    Optional[Any]						     = np.sin(snake_case__       )
		A_		:    Dict						     = theta_a * t
		A_		:    Union[str, Any]						     = np.sin(snake_case__       )
		A_		:    Optional[int]						     = np.sin(theta_a - theta_t       ) / sin_theta_a
		A_		:    Optional[Any]						     = sin_theta_t / sin_theta_a
		A_		:    Any						     = sa * va + sa * va

	if inputs_are_torch:
		A_		:    str						     = torch.from_numpy(snake_case__       ).to(snake_case__       )

	return va






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Dict						     = F.normalize(snake_case__ , dim=-1       )
	A_		:    Any						     = F.normalize(snake_case__ , dim=-1       )
	return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	for param in model.parameters():
		A_		:    Optional[int]						     = value




class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  ):
		super().__init__()
		self.register_modules(
		    vae=lowerCAmelCase_  ,  text_encoder=lowerCAmelCase_  ,  clip_model=lowerCAmelCase_  ,  tokenizer=lowerCAmelCase_  ,  unet=lowerCAmelCase_  ,  scheduler=lowerCAmelCase_  ,  feature_extractor=lowerCAmelCase_  ,  coca_model=lowerCAmelCase_  ,  coca_tokenizer=lowerCAmelCase_  ,  coca_transform=lowerCAmelCase_  ,  )
		A_		:    Tuple						     = (
		    feature_extractor.size
		    if isinstance(feature_extractor.size  ,  lowerCAmelCase_				)
		    else feature_extractor.size["""shortest_edge"""]
		)
		A_		:    int						     = transforms.Normalize(mean=feature_extractor.image_mean  ,  std=feature_extractor.image_std				)
		set_requires_grad(self.text_encoder  ,  lowerCAmelCase_				)
		set_requires_grad(self.clip_model  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_ = "auto"				):
		if slice_size == "auto":
			# half the attention head size is usually a good trade-off between
			# speed and memory
			A_		:    Optional[int]						     = self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.enable_attention_slicing(lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		set_requires_grad(self.vae  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		set_requires_grad(self.vae  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		set_requires_grad(self.unet  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		set_requires_grad(self.unet  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		# get the original timestep using init_timestep
		A_		:    Any						     = min(int(num_inference_steps * strength				)  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = max(num_inference_steps - init_timestep  ,  0				)
		A_		:    List[Any]						     = self.scheduler.timesteps[t_start:]

		return timesteps, num_inference_steps - t_start





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None				):
		if not isinstance(lowerCAmelCase_  ,  torch.Tensor				):
			raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowerCAmelCase_				)}"""				)

		A_		:    Tuple						     = image.to(device=lowerCAmelCase_  ,  dtype=lowerCAmelCase_				)

		if isinstance(lowerCAmelCase_  ,  lowerCAmelCase_				):
			A_		:    Tuple						     = [
			    self.vae.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(lowerCAmelCase_				)
			]
			A_		:    str						     = torch.cat(lowerCAmelCase_  ,  dim=0				)
		else:
			A_		:    Dict						     = self.vae.encode(lowerCAmelCase_				).latent_dist.sample(lowerCAmelCase_				)

		# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
		A_		:    List[Any]						     = 0.18215 * init_latents
		A_		:    Optional[int]						     = init_latents.repeat_interleave(lowerCAmelCase_  ,  dim=0				)

		A_		:    Optional[int]						     = randn_tensor(init_latents.shape  ,  generator=lowerCAmelCase_  ,  device=lowerCAmelCase_  ,  dtype=lowerCAmelCase_				)

		# get latents
		A_		:    Tuple						     = self.scheduler.add_noise(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    List[Any]						     = init_latents

		return latents





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.coca_transform(lowerCAmelCase_				).unsqueeze(0				)
		with torch.no_grad(), torch.cuda.amp.autocast():
			A_		:    Any						     = self.coca_model.generate(transformed_image.to(device=self.device  ,  dtype=self.coca_model.dtype				)				)
		A_		:    str						     = self.coca_tokenizer.decode(generated[0].cpu().numpy()				)
		return generated.split("""<end_of_text>"""				)[0].replace("""<start_of_text>"""  ,  """"""				).rstrip(""" .,"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Any						     = self.feature_extractor.preprocess(lowerCAmelCase_				)
		A_		:    Tuple						     = torch.from_numpy(clip_image_input["""pixel_values"""][0]				).unsqueeze(0				).to(self.device				).half()
		A_		:    Optional[int]						     = self.clip_model.get_image_features(lowerCAmelCase_				)
		A_		:    Dict						     = image_embeddings_clip / image_embeddings_clip.norm(p=2  ,  dim=-1  ,  keepdim=lowerCAmelCase_				)
		A_		:    List[str]						     = image_embeddings_clip.repeat_interleave(lowerCAmelCase_  ,  dim=0				)
		return image_embeddings_clip





	@torch.enable_grad()
	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		A_		:    List[str]						     = latents.detach().requires_grad_()

		A_		:    Dict						     = self.scheduler.scale_model_input(lowerCAmelCase_  ,  lowerCAmelCase_				)

		# predict the noise residual
		A_		:    Any						     = self.unet(lowerCAmelCase_  ,  lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_				).sample

		if isinstance(self.scheduler  ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
			A_		:    Tuple						     = self.scheduler.alphas_cumprod[timestep]
			A_		:    Tuple						     = 1 - alpha_prod_t
			# compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			A_		:    Tuple						     = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

			A_		:    Union[str, Any]						     = torch.sqrt(lowerCAmelCase_				)
			A_		:    List[str]						     = pred_original_sample * (fac) + latents * (1 - fac)
		elif isinstance(self.scheduler  ,  lowerCAmelCase_				):
			A_		:    int						     = self.scheduler.sigmas[index]
			A_		:    int						     = latents - sigma * noise_pred
		else:
			raise ValueError(f"""scheduler type {type(self.scheduler				)} not supported"""				)

		# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
		A_		:    int						     = 1 / 0.18215 * sample
		A_		:    Optional[int]						     = self.vae.decode(lowerCAmelCase_				).sample
		A_		:    Optional[Any]						     = (image / 2 + 0.5).clamp(0  ,  1				)

		A_		:    str						     = transforms.Resize(self.feature_extractor_size				)(lowerCAmelCase_				)
		A_		:    int						     = self.normalize(lowerCAmelCase_				).to(latents.dtype				)

		A_		:    Union[str, Any]						     = self.clip_model.get_image_features(lowerCAmelCase_				)
		A_		:    Dict						     = image_embeddings_clip / image_embeddings_clip.norm(p=2  ,  dim=-1  ,  keepdim=lowerCAmelCase_				)

		A_		:    Tuple						     = spherical_dist_loss(lowerCAmelCase_  ,  lowerCAmelCase_				).mean() * clip_guidance_scale

		A_		:    List[Any]						     = -torch.autograd.grad(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]

		if isinstance(self.scheduler  ,  lowerCAmelCase_				):
			A_		:    Tuple						     = latents.detach() + grads * (sigma**2)
			A_		:    int						     = noise_pred_original
		else:
			A_		:    Tuple						     = noise_pred_original - torch.sqrt(lowerCAmelCase_				) * grads
		return noise_pred, latents





	@torch.no_grad()
	def __call__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 512  ,  lowerCAmelCase_ = 512  ,  lowerCAmelCase_ = 0.6  ,  lowerCAmelCase_ = 50  ,  lowerCAmelCase_ = 7.5  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = 0.0  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "pil"  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = 0.8  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  ):
		if isinstance(lowerCAmelCase_  ,  lowerCAmelCase_				) and len(lowerCAmelCase_				) != batch_size:
			raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowerCAmelCase_				)} generators."""				)

		if height % 8 != 0 or width % 8 != 0:
			raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""				)

		if isinstance(lowerCAmelCase_  ,  torch.Generator				) and batch_size > 1:
			A_		:    str						     = [generator] + [None] * (batch_size - 1)

		A_		:    List[Any]						     = [
		    ("""model""", self.coca_model is None),
		    ("""tokenizer""", self.coca_tokenizer is None),
		    ("""transform""", self.coca_transform is None),
		]
		A_		:    Dict						     = [x[0] for x in coca_is_none if x[1]]
		A_		:    Union[str, Any]						     = """, """.join(lowerCAmelCase_				)
		# generate prompts with coca model if prompt is None
		if content_prompt is None:
			if len(lowerCAmelCase_				):
				raise ValueError(
				    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
				    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""				)
			A_		:    Any						     = self.get_image_description(lowerCAmelCase_				)
		if style_prompt is None:
			if len(lowerCAmelCase_				):
				raise ValueError(
				    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
				    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""				)
			A_		:    List[str]						     = self.get_image_description(lowerCAmelCase_				)

		# get prompt text embeddings for content and style
		A_		:    Any						     = self.tokenizer(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=self.tokenizer.model_max_length  ,  truncation=lowerCAmelCase_  ,  return_tensors="""pt"""  ,  )
		A_		:    int						     = self.text_encoder(content_text_input.input_ids.to(self.device				)				)[0]

		A_		:    Optional[int]						     = self.tokenizer(
		    lowerCAmelCase_  ,  padding="""max_length"""  ,  max_length=self.tokenizer.model_max_length  ,  truncation=lowerCAmelCase_  ,  return_tensors="""pt"""  ,  )
		A_		:    Tuple						     = self.text_encoder(style_text_input.input_ids.to(self.device				)				)[0]

		A_		:    Any						     = slerp(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

		# duplicate text embeddings for each generation per prompt
		A_		:    Any						     = text_embeddings.repeat_interleave(lowerCAmelCase_  ,  dim=0				)

		# set timesteps
		A_		:    List[str]						     = """offset""" in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
		A_		:    int						     = {}
		if accepts_offset:
			A_		:    Dict						     = 1

		self.scheduler.set_timesteps(lowerCAmelCase_  ,  **lowerCAmelCase_				)
		# Some schedulers like PNDM have timesteps as arrays
		# It's more optimized to move all timesteps to correct device beforehand
		self.scheduler.timesteps.to(self.device				)

		A_     ,  A_		:    str						     = self.get_timesteps(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.device				)
		A_		:    List[str]						     = timesteps[:1].repeat(lowerCAmelCase_				)

		# Preprocess image
		A_		:    Tuple						     = preprocess(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    List[str]						     = self.prepare_latents(
		    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  text_embeddings.dtype  ,  self.device  ,  lowerCAmelCase_				)

		A_		:    Tuple						     = preprocess(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    List[Any]						     = self.prepare_latents(
		    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  text_embeddings.dtype  ,  self.device  ,  lowerCAmelCase_				)

		A_		:    Optional[int]						     = slerp(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

		if clip_guidance_scale > 0:
			A_		:    Any						     = self.get_clip_image_embeddings(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[Any]						     = self.get_clip_image_embeddings(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    str						     = slerp(
			    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

		# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
		# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
		# corresponds to doing no classifier free guidance.
		A_		:    Any						     = guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			A_		:    str						     = content_text_input.input_ids.shape[-1]
			A_		:    Dict						     = self.tokenizer([""""""]  ,  padding="""max_length"""  ,  max_length=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			A_		:    Union[str, Any]						     = self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
			# duplicate unconditional embeddings for each generation per prompt
			A_		:    str						     = uncond_embeddings.repeat_interleave(lowerCAmelCase_  ,  dim=0				)

			# For classifier free guidance, we need to do two forward passes.
			# Here we concatenate the unconditional and text embeddings into a single batch
			# to avoid doing two forward passes
			A_		:    Dict						     = torch.cat([uncond_embeddings, text_embeddings]				)

		# get the initial random noise unless the user supplied it

		# Unlike in other pipelines, latents need to be generated in the target device
		# for 1-to-1 results reproducibility with the CompVis implementation.
		# However this currently doesn't work in `mps`.
		A_		:    Any						     = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
		A_		:    List[str]						     = text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not work reproducibly on mps
				A_		:    Union[str, Any]						     = torch.randn(lowerCAmelCase_  ,  generator=lowerCAmelCase_  ,  device="""cpu"""  ,  dtype=lowerCAmelCase_				).to(
				    self.device				)
			else:
				A_		:    Tuple						     = torch.randn(lowerCAmelCase_  ,  generator=lowerCAmelCase_  ,  device=self.device  ,  dtype=lowerCAmelCase_				)
		else:
			if latents.shape != latents_shape:
				raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
			A_		:    Dict						     = latents.to(self.device				)

		# scale the initial noise by the standard deviation required by the scheduler
		A_		:    Optional[Any]						     = latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		A_		:    Optional[Any]						     = """eta""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
		A_		:    Optional[int]						     = {}
		if accepts_eta:
			A_		:    Any						     = eta

		# check if the scheduler accepts generator
		A_		:    Union[str, Any]						     = """generator""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
		if accepts_generator:
			A_		:    int						     = generator

		with self.progress_bar(total=lowerCAmelCase_				):
			for i, t in enumerate(lowerCAmelCase_				):
				# expand the latents if we are doing classifier free guidance
				A_		:    Any						     = torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
				A_		:    str						     = self.scheduler.scale_model_input(lowerCAmelCase_  ,  lowerCAmelCase_				)

				# predict the noise residual
				A_		:    Optional[int]						     = self.unet(lowerCAmelCase_  ,  lowerCAmelCase_  ,  encoder_hidden_states=lowerCAmelCase_				).sample

				# perform classifier free guidance
				if do_classifier_free_guidance:
					A_     ,  A_		:    Union[str, Any]						     = noise_pred.chunk(2				)
					A_		:    int						     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# perform clip guidance
				if clip_guidance_scale > 0:
					A_		:    Union[str, Any]						     = (
					    text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
					)
					A_     ,  A_		:    Union[str, Any]						     = self.cond_fn(
					    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  )

				# compute the previous noisy sample x_t -> x_t-1
				A_		:    List[Any]						     = self.scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
		A_		:    Tuple						     = 1 / 0.18215 * latents
		A_		:    Tuple						     = self.vae.decode(lowerCAmelCase_				).sample

		A_		:    Optional[int]						     = (image / 2 + 0.5).clamp(0  ,  1				)
		A_		:    str						     = image.cpu().permute(0  ,  2  ,  3  ,  1				).numpy()

		if output_type == "pil":
			A_		:    Optional[int]						     = self.numpy_to_pil(lowerCAmelCase_				)

		if not return_dict:
			return (image, None)

		return StableDiffusionPipelineOutput(images=lowerCAmelCase_  ,  nsfw_content_detected=lowerCAmelCase_				)









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

import datasets


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


_lowerCAmelCase       =				"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


_lowerCAmelCase       =				r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"



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





	"""simple docstring"""




	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  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = 0.0
		for i, j in zip(lowerCAmelCase_  ,  lowerCAmelCase_				):
			n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_  ,  lowerCAmelCase_				) else 0.0
		A_		:    List[str]						     = n_correct / len(lowerCAmelCase_				)
		return {
		    "accuracy": accuracy,
		}

