





"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Any			,       a_					:		List[Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  OmegaConf.load(a_							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.load(a_			,       map_location='''cpu'''							)['''model''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  list(state_dict.keys()							)

      # extract state_dict for VQVAE
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {}
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''first_stage_model.'''
      for key in keys:
            if key.startswith(a_							):
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  state_dict[key]

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

      __SCREAMING_SNAKE_CASE       :int		    =  config.model.params.first_stage_config.params
      __SCREAMING_SNAKE_CASE       :Tuple		    =  config.model.params.unet_config.params

      __SCREAMING_SNAKE_CASE       :str		    =  VQModel(**a_							).eval()
      vqvae.load_state_dict(a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  UNetLDMModel(**a_							).eval()
      unet.load_state_dict(a_							)

      __SCREAMING_SNAKE_CASE       :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=a_			,       )

      __SCREAMING_SNAKE_CASE       :int		    =  LDMPipeline(a_			,       a_			,       a_							)
      pipeline.save_pretrained(a_							)


if __name__ == "__main__":
   lowerCamelCase_       =		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)
   lowerCamelCase_       =		parser.parse_args()

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






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Dict		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :Any		    =  image_size
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :Any		    =  tubelet_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)