





'''simple docstring'''

import heapq as hq
import math
from collections.abc import Iterator

class    UpperCAmelCase			:




		def __init__(self   :						List[Any]				,      A__   :						Tuple					)       ->			Optional[Any]:
						lowercase    =			str(id_					)
						lowercase    =			None
						lowercase    =			None
						lowercase    =			[]
						lowercase    =			{}  # {vertex:distance}




		def __lt__(self   :						List[str]				,      A__   :						Tuple					)       ->			Optional[int]:
						return self.key < other.key




		def __repr__(self   :						int					)       ->			Dict:
						return self.id




		def 					UpperCAmelCase__ (self   :						str				,      A__   :						str					)       ->			Any:
						self.neighbors.append(_snake_case					)





		def 					UpperCAmelCase__ (self   :						Tuple				,      A__   :						str				,      A__   :						Optional[Any]					)       ->			Tuple:
						lowercase    =			weight







def 				UpperCAmelCase_     (						lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_				):

				"""simple docstring"""






				graph[a - 1].add_neighbor(graph[b - 1]				)
				graph[b - 1].add_neighbor(graph[a - 1]				)
				# add the edges:
				graph[a - 1].add_edge(graph[b - 1]		,				__UpperCamelCase				)
				graph[b - 1].add_edge(graph[a - 1]		,				__UpperCamelCase				)







def 				UpperCAmelCase_     (						lowerCAmelCase_		,				lowerCAmelCase_				):

				"""simple docstring"""






				lowercase    =			[]
				for u in graph:
								lowercase    =			math.inf
								lowercase    =			None
				lowercase    =			0
				lowercase    =			graph[:]
				while q:
								lowercase    =			min(__UpperCamelCase				)
								q.remove(__UpperCamelCase				)
								for v in u.neighbors:
												if (v in q) and (u.edges[v.id] < v.key):
																lowercase    =			u
																lowercase    =			u.edges[v.id]
				for i in range(1		,				len(__UpperCamelCase				)				):
								a.append((int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)				)
				return a







def 				UpperCAmelCase_     (						lowerCAmelCase_		,				lowerCAmelCase_				):

				"""simple docstring"""






				for u in graph:
								lowercase    =			math.inf
								lowercase    =			None
				lowercase    =			0

				lowercase    =			list(__UpperCamelCase				)
				hq.heapify(__UpperCamelCase				)

				while h:
								lowercase    =			hq.heappop(__UpperCamelCase				)
								for v in u.neighbors:
												if (v in h) and (u.edges[v.id] < v.key):
																lowercase    =			u
																lowercase    =			u.edges[v.id]
																hq.heapify(__UpperCamelCase				)

				for i in range(1		,				len(__UpperCamelCase				)				):
								yield (int(graph[i].id				) + 1, int(graph[i].pi.id				) + 1)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

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

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

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     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__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


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)




