


'''simple docstring'''
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		List[Any]													=				OmegaConf.load(lowerCAmelCase_						)
    UpperCAmelCase__			:		Union[str, Any]													=				torch.load(lowerCAmelCase_  ,					map_location='''cpu'''						)['''model''']
    UpperCAmelCase__			:		Union[str, Any]													=				list(state_dict.keys()						)

    # extract state_dict for VQVAE
    UpperCAmelCase__			:		int													=				{}
    UpperCAmelCase__			:		int													=				'''first_stage_model.'''
    for key in keys:
        if key.startswith(lowerCAmelCase_						):
            UpperCAmelCase__			:		Union[str, Any]													=				state_dict[key]

    # extract state_dict for UNetLDM
    UpperCAmelCase__			:		Tuple													=				{}
    UpperCAmelCase__			:		int													=				'''model.diffusion_model.'''
    for key in keys:
        if key.startswith(lowerCAmelCase_						):
            UpperCAmelCase__			:		str													=				state_dict[key]

    UpperCAmelCase__			:		int													=				config.model.params.first_stage_config.params
    UpperCAmelCase__			:		List[Any]													=				config.model.params.unet_config.params

    UpperCAmelCase__			:		int													=				VQModel(**lowerCAmelCase_						).eval()
    vqvae.load_state_dict(lowerCAmelCase_						)

    UpperCAmelCase__			:		str													=				UNetLDMModel(**lowerCAmelCase_						).eval()
    unet.load_state_dict(lowerCAmelCase_						)

    UpperCAmelCase__			:		int													=				DDIMScheduler(
        timesteps=config.model.params.timesteps  ,					beta_schedule='''scaled_linear'''  ,					beta_start=config.model.params.linear_start  ,					beta_end=config.model.params.linear_end  ,					clip_sample=lowerCAmelCase_  ,					)

    UpperCAmelCase__			:		List[str]													=				LDMPipeline(lowerCAmelCase_  ,					lowerCAmelCase_  ,					lowerCAmelCase_						)
    pipeline.save_pretrained(lowerCAmelCase_						)


if __name__ == "__main__":
  UpperCamelCase__    					=      argparse.ArgumentParser()
  parser.add_argument('''--checkpoint_path''', type=str, required=True)
  parser.add_argument('''--config_path''', type=str, required=True)
  parser.add_argument('''--output_path''', type=str, required=True)
  UpperCamelCase__    					=      parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)







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,
     }

