


'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	_UpperCamelCase      ):






      def __init__(					self				:    Optional[int]						,						_A				:    AutoencoderKL						,						_A				:    CLIPTextModel						,						_A				:    CLIPTokenizer						,						_A				:    UNetaDConditionModel						,						_A				:    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,						_A				:    StableDiffusionSafetyChecker						,						_A				:    CLIPImageProcessor						,						):


          '''simple docstring'''


          super().__init__()
          self.register_modules(
              vae=lowerCAmelCase_						,						text_encoder=lowerCAmelCase_						,						tokenizer=lowerCAmelCase_						,						unet=lowerCAmelCase_						,						scheduler=lowerCAmelCase_						,						safety_checker=lowerCAmelCase_						,						feature_extractor=lowerCAmelCase_						,						)






      def lowercase_							(					self				:    int						,						_A				:    Optional[Union[str, int]] = "auto" ):


          '''simple docstring'''


          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              UpperCAmelCase__			:		Dict													=				self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCAmelCase_ )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          self.enable_attention_slicing(lowerCAmelCase_ )






      @torch.no_grad()
      def __call__(					self				:    Tuple						,						_A				:    Union[str, List[str]]						,						_A				:    int = 512						,						_A				:    int = 512						,						_A				:    int = 50						,						_A				:    float = 7.5						,						_A				:    Optional[Union[str, List[str]]] = None						,						_A				:    Optional[int] = 1						,						_A				:    float = 0.0						,						_A				:    Optional[torch.Generator] = None						,						_A				:    Optional[torch.FloatTensor] = None						,						_A				:    Optional[str] = "pil"						,						_A				:    bool = True						,						_A				:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,						_A				:    int = 1						,						_A				:    Optional[torch.FloatTensor] = None						,						**_A				:    Optional[Any]						,						):


          '''simple docstring'''


          if isinstance(lowerCAmelCase_						,						lowerCAmelCase_ ):
              UpperCAmelCase__			:		str													=				1
          elif isinstance(lowerCAmelCase_						,						lowerCAmelCase_ ):
              UpperCAmelCase__			:		List[str]													=				len(lowerCAmelCase_ )
          else:
              raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_ )}""" )

          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 (callback_steps is None) or (
              callback_steps is not None and (not isinstance(lowerCAmelCase_						,						lowerCAmelCase_ ) or callback_steps <= 0)
          ):
              raise ValueError(
                  f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                  f""" {type(lowerCAmelCase_ )}.""" )

          # get prompt text embeddings
          UpperCAmelCase__			:		Optional[int]													=				self.tokenizer(
              lowerCAmelCase_						,						padding='''max_length'''						,						max_length=self.tokenizer.model_max_length						,						return_tensors='''pt'''						,						)
          UpperCAmelCase__			:		Union[str, Any]													=				text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              UpperCAmelCase__			:		Optional[Any]													=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
              logger.warning(
                  '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                  f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
              UpperCAmelCase__			:		str													=				text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
              UpperCAmelCase__			:		Union[str, Any]													=				self.text_encoder(text_input_ids.to(self.device ) )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				text_embeddings.shape
          UpperCAmelCase__			:		List[Any]													=				text_embeddings.repeat(1						,						lowerCAmelCase_						,						1 )
          UpperCAmelCase__			:		Optional[int]													=				text_embeddings.view(bs_embed * num_images_per_prompt						,						lowerCAmelCase_						,						-1 )

          # 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.
          UpperCAmelCase__			:		str													=				guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
              UpperCAmelCase__			:		Optional[int]													=				42
              if negative_prompt is None:
                  UpperCAmelCase__			:		Tuple													=				['''''']
              elif type(lowerCAmelCase_ ) is not type(lowerCAmelCase_ ):
                  raise TypeError(
                      f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_ )} !="""
                      f""" {type(lowerCAmelCase_ )}.""" )
              elif isinstance(lowerCAmelCase_						,						lowerCAmelCase_ ):
                  UpperCAmelCase__			:		List[Any]													=				[negative_prompt]
              elif batch_size != len(lowerCAmelCase_ ):
                  raise ValueError(
                      f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_ )}, but `prompt`:"""
                      f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                      ''' the batch size of `prompt`.''' )
              else:
                  UpperCAmelCase__			:		Optional[Any]													=				negative_prompt

              UpperCAmelCase__			:		Optional[int]													=				text_input_ids.shape[-1]
              UpperCAmelCase__			:		Tuple													=				self.tokenizer(
                  lowerCAmelCase_						,						padding='''max_length'''						,						max_length=lowerCAmelCase_						,						truncation=lowerCAmelCase_						,						return_tensors='''pt'''						,						)
              UpperCAmelCase__			:		int													=				self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              UpperCAmelCase__			:		str													=				uncond_embeddings.shape[1]
              UpperCAmelCase__			:		str													=				uncond_embeddings.repeat(lowerCAmelCase_						,						lowerCAmelCase_						,						1 )
              UpperCAmelCase__			:		Dict													=				uncond_embeddings.view(batch_size * num_images_per_prompt						,						lowerCAmelCase_						,						-1 )

              # 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
              UpperCAmelCase__			:		Tuple													=				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`.
          UpperCAmelCase__			:		str													=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          UpperCAmelCase__			:		Any													=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
          UpperCAmelCase__			:		List[str]													=				text_embeddings.dtype
          if latents is None:
              if self.device.type == "mps":
                  # randn does not exist on mps
                  UpperCAmelCase__			:		Optional[int]													=				torch.randn(
                      lowerCAmelCase_						,						generator=lowerCAmelCase_						,						device='''cpu'''						,						dtype=lowerCAmelCase_ ).to(self.device )
                  UpperCAmelCase__			:		List[str]													=				torch.randn(lowerCAmelCase_						,						generator=lowerCAmelCase_						,						device='''cpu'''						,						dtype=lowerCAmelCase_ ).to(
                      self.device )
              else:
                  UpperCAmelCase__			:		int													=				torch.randn(
                      lowerCAmelCase_						,						generator=lowerCAmelCase_						,						device=self.device						,						dtype=lowerCAmelCase_ )
                  UpperCAmelCase__			:		Tuple													=				torch.randn(lowerCAmelCase_						,						generator=lowerCAmelCase_						,						device=self.device						,						dtype=lowerCAmelCase_ )
          else:
              if latents_reference.shape != latents_shape:
                  raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
              UpperCAmelCase__			:		Any													=				latents_reference.to(self.device )
              UpperCAmelCase__			:		Optional[int]													=				latents.to(self.device )

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          UpperCAmelCase__			:		Optional[Any]													=				(latents_shape[3] - latents_shape_reference[3]) // 2
          UpperCAmelCase__			:		Optional[Any]													=				(latents_shape[2] - latents_shape_reference[2]) // 2
          UpperCAmelCase__			:		Any													=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          UpperCAmelCase__			:		int													=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          UpperCAmelCase__			:		Dict													=				0 if dx < 0 else dx
          UpperCAmelCase__			:		Optional[int]													=				0 if dy < 0 else dy
          UpperCAmelCase__			:		List[str]													=				max(-dx						,						0 )
          UpperCAmelCase__			:		Dict													=				max(-dy						,						0 )
          # import pdb
          # pdb.set_trace()
          UpperCAmelCase__			:		Union[str, Any]													=				latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(lowerCAmelCase_ )

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          UpperCAmelCase__			:		Optional[Any]													=				self.scheduler.timesteps.to(self.device )

          # scale the initial noise by the standard deviation required by the scheduler
          UpperCAmelCase__			:		Optional[int]													=				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]
          UpperCAmelCase__			:		Optional[int]													=				'''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
          UpperCAmelCase__			:		int													=				{}
          if accepts_eta:
              UpperCAmelCase__			:		List[Any]													=				eta

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

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

              # perform guidance
              if do_classifier_free_guidance:
                  UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				noise_pred.chunk(2 )
                  UpperCAmelCase__			:		Optional[int]													=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase__			:		Tuple													=				self.scheduler.step(lowerCAmelCase_						,						lowerCAmelCase_						,						lowerCAmelCase_						,						**lowerCAmelCase_ ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                  callback(lowerCAmelCase_						,						lowerCAmelCase_						,						lowerCAmelCase_ )

          UpperCAmelCase__			:		List[Any]													=				1 / 0.1_8_2_1_5 * latents
          UpperCAmelCase__			:		Dict													=				self.vae.decode(lowerCAmelCase_ ).sample

          UpperCAmelCase__			:		Any													=				(image / 2 + 0.5).clamp(0						,						1 )

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          UpperCAmelCase__			:		Dict													=				image.cpu().permute(0						,						2						,						3						,						1 ).float().numpy()

          if self.safety_checker is not None:
              UpperCAmelCase__			:		List[str]													=				self.feature_extractor(self.numpy_to_pil(lowerCAmelCase_ )						,						return_tensors='''pt''' ).to(
                  self.device )
              UpperCAmelCase__						,						UpperCAmelCase__			:		List[Any]													=				self.safety_checker(
                  images=lowerCAmelCase_						,						clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
          else:
              UpperCAmelCase__			:		int													=				None

          if output_type == "pil":
              UpperCAmelCase__			:		List[str]													=				self.numpy_to_pil(lowerCAmelCase_ )

          if not return_dict:
              return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=lowerCAmelCase_						,						nsfw_content_detected=lowerCAmelCase_ )







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

import datasets


_snake_case  :    Tuple						    =  '\\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'


_snake_case  :    str						    =  '\\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'


_snake_case  :    List[str]						    =  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 				_UpperCAmelCase    (    datasets.Metric    ):

  """simple docstring"""


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

