





'''simple docstring'''



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							OmegaConf.load(lowerCamelCase_			)
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)['''model''']
			__lowercase			  =							list(state_dict.keys()			)

			# extract state_dict for VQVAE
			__lowercase			  =							{}
			__lowercase			  =							'''first_stage_model.'''
			for key in keys:
						if key.startswith(lowerCamelCase_			):
									__lowercase			  =							state_dict[key]

    # extract state_dict for UNetLDM
			__lowercase			  =							{}
			__lowercase			  =							'''model.diffusion_model.'''
			for key in keys:
						if key.startswith(lowerCamelCase_			):
									__lowercase			  =							state_dict[key]

			__lowercase			  =							config.model.params.first_stage_config.params
			__lowercase			  =							config.model.params.unet_config.params

			__lowercase			  =							VQModel(**lowerCamelCase_			).eval()
			vqvae.load_state_dict(lowerCamelCase_			)

			__lowercase			  =							UNetLDMModel(**lowerCamelCase_			).eval()
			unet.load_state_dict(lowerCamelCase_			)

			__lowercase			  =							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=lowerCamelCase_						,    )

			__lowercase			  =							LDMPipeline(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			pipeline.save_pretrained(lowerCamelCase_			)


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)












'''simple docstring'''



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =      {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FocalNetForImageClassification''',
	    '''FocalNetForMaskedImageModeling''',
	    '''FocalNetBackbone''',
	    '''FocalNetModel''',
	    '''FocalNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_focalnet import (
		    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FocalNetBackbone,
		    FocalNetForImageClassification,
		    FocalNetForMaskedImageModeling,
		    FocalNetModel,
		    FocalNetPreTrainedModel,
		)

else:
	import sys

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





