





"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'M-CLIP'

      def __init__(							self       :      Optional[Any]						,				__lowercase       :      str=1_024						,				__lowercase       :      Dict=768						,				**__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						transformerDimSize
        __UpperCAmelCase    :						Optional[Any]       =						imageDimSize
        super().__init__(**__lowercase )




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Optional[int]         =       MCLIPConfig

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      str						,				*__lowercase       :      List[Any]						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        super().__init__(__lowercase						,				*__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						List[Any]       =						XLMRobertaModel(__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						torch.nn.Linear(
            in_features=config.transformerDimensions						,				out_features=config.numDims )







      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Dict						,				__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.transformer(input_ids=__lowercase						,				attention_mask=__lowercase )[0]
        __UpperCAmelCase    :						List[Any]       =						(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
        return self.LinearTransformation(__lowercase ), embs






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						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=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       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)
  lowercase__				:Dict				   =       parser.parse_args()

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