
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 				a						(							snake_case__:					Any						,   snake_case__:					Union[str, Any]						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					OmegaConf.load(snake_case__					)
				lowercase_       =					torch.load(snake_case__						,   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(snake_case__					):
												lowercase_       =					state_dict[key]

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

				lowercase_       =					config.model.params.first_stage_config.params
				lowercase_       =					config.model.params.unet_config.params

				lowercase_       =					VQModel(**snake_case__					).eval()
				vqvae.load_state_dict(snake_case__					)

				lowercase_       =					UNetLDMModel(**snake_case__					).eval()
				unet.load_state_dict(snake_case__					)

				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=snake_case__						,   )

				lowercase_       =					LDMPipeline(snake_case__						,   snake_case__						,   snake_case__					)
				pipeline.save_pretrained(snake_case__					)


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)






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




