





"""simple docstring"""





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__				:List[str]				   =       logging.get_logger(__name__)

lowercase__				:Optional[int]				   =       {"vocab_file": "vocab.json"}

lowercase__				:Dict				   =       {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowercase__				:Optional[Any]				   =       {"mgp-str": 2_7}




class 							snake_case							(			_UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Optional[int]         =       VOCAB_FILES_NAMES
      _A     :       Optional[int]         =       PRETRAINED_VOCAB_FILES_MAP
      _A     :       Optional[int]         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

      def __init__(							self       :      int						,				__lowercase       :      Optional[int]						,				__lowercase       :      Optional[int]="[GO]"						,				__lowercase       :      int="[GO]"						,				__lowercase       :      str="[s]"						,				__lowercase       :      Dict="[GO]"						,				**__lowercase       :      List[str] ):


        '''simple docstring'''




        super().__init__(
            unk_token=lowerCamelCase_						,				bos_token=lowerCamelCase_						,				eos_token=lowerCamelCase_						,				pad_token=lowerCamelCase_						,				**lowerCamelCase_						,				)

        with open(lowerCamelCase_						,				encoding='''utf-8''' ) as vocab_handle:
          __UpperCAmelCase    :						int       =						json.load(lowerCamelCase_ )
        __UpperCAmelCase    :						Union[str, Any]       =						{v: k for k, v in self.vocab.items()}

      @property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        return len(self.vocab )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        return dict(self.vocab						,				**self.added_tokens_encoder )

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


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						[]
        for s in text:
          char_tokens.extend(lowerCamelCase_ )
        return char_tokens

      def       A_					(							self       :      Dict						,				__lowercase       :      Tuple ):


        '''simple docstring'''




        return self.vocab.get(lowerCamelCase_						,				self.vocab.get(self.unk_token ) )

      def       A_					(							self       :      List[str]						,				__lowercase       :      Dict ):


        '''simple docstring'''




        return self.decoder.get(lowerCamelCase_ )







      def       A_					(							self       :      Optional[int]						,				__lowercase       :      str						,				__lowercase       :      Optional[str] = None ):


        '''simple docstring'''




        if not os.path.isdir(lowerCamelCase_ ):
          logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_ ) )
          return
        __UpperCAmelCase    :						List[Any]       =						os.path.join(
            lowerCamelCase_						,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        with open(lowerCamelCase_						,				'''w'''						,				encoding='''utf-8''' ) as f:
          f.write(json.dumps(self.vocab						,				indent=2						,				sort_keys=lowerCamelCase_						,				ensure_ascii=lowerCamelCase_ ) + '''\n''' )

        return (vocab_file,)






"""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)