






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def      _lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							Optional[Any]		,		__A    :							Dict )      ->					Optional[int]:
    _UpperCAmelCase				:      List[Any]			  =				OmegaConf.load(__A )
    _UpperCAmelCase				:      Any			  =				torch.load(__A		,		map_location='''cpu''' )['''model''']
    _UpperCAmelCase				:      Optional[int]			  =				list(state_dict.keys() )

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

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

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

    _UpperCAmelCase				:      Dict			  =				VQModel(**__A ).eval()
    vqvae.load_state_dict(__A )

    _UpperCAmelCase				:      Dict			  =				UNetLDMModel(**__A ).eval()
    unet.load_state_dict(__A )

    _UpperCAmelCase				:      str			  =				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		,		)

    _UpperCAmelCase				:      Optional[Any]			  =				LDMPipeline(__A		,		__A		,		__A )
    pipeline.save_pretrained(__A )


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

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












from __future__ import annotations

def      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    _UpperCAmelCase				:      List[str]			  =				[True] * limit
    _UpperCAmelCase				:      Optional[int]			  =				False
    _UpperCAmelCase				:      Dict			  =				False
    _UpperCAmelCase				:      List[str]			  =				True

    for i in range(3		,		int(limit**0.5 + 1 )		,		2 ):
        _UpperCAmelCase				:      List[str]			  =				i * 2
        while index < limit:
            _UpperCAmelCase				:      Union[str, Any]			  =				False
            _UpperCAmelCase				:      int			  =				index + i

    _UpperCAmelCase				:      Optional[int]			  =				[2]

    for i in range(3		,		__A		,		2 ):
        if is_prime[i]:
            primes.append(__A )

    return primes

def      _lowerCamelCase	(						__A    :							int = 1_000_000 )      ->					int:
    _UpperCAmelCase				:      Any			  =				prime_sieve(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    _UpperCAmelCase				:      Tuple			  =				0

    for i in range(len(__A ) ):
        for j in range(i + length		,		len(__A ) ):
            _UpperCAmelCase				:      List[Any]			  =				sum(primes[i:j] )
            if sol >= ceiling:
                break

            if sol in primes:
                _UpperCAmelCase				:      List[str]			  =				j - i
                _UpperCAmelCase				:      Optional[Any]			  =				sol

    return largest


if __name__ == "__main__":
  print(F'{solution() = }')




