






"""simple docstring"""



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						A__						:  Dict		  =  OmegaConf.load(UpperCAmelCase__      )
						A__						:  Optional[int]		  =  torch.load(UpperCAmelCase__,						map_location="""cpu"""      )["""model"""]
						A__						:  str		  =  list(state_dict.keys()      )

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

    # extract state_dict for UNetLDM
						A__						:  Dict		  =  {}
						A__						:  Optional[int]		  =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(UpperCAmelCase__      ):
																		A__						:  List[Any]		  =  state_dict[key]

						A__						:  Optional[Any]		  =  config.model.params.first_stage_config.params
						A__						:  Dict		  =  config.model.params.unet_config.params

						A__						:  Optional[Any]		  =  VQModel(**UpperCAmelCase__      ).eval()
						vqvae.load_state_dict(UpperCAmelCase__      )

						A__						:  Tuple		  =  UNetLDMModel(**UpperCAmelCase__      ).eval()
						unet.load_state_dict(UpperCAmelCase__      )

						A__						:  Dict		  =  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__,						)

						A__						:  Tuple		  =  LDMPipeline(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
						pipeline.save_pretrained(UpperCAmelCase__      )


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

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














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






