




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			OmegaConf.load(snake_case       )
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )["""model"""]
						_UpperCAmelCase      =			list(state_dict.keys()       )

						# extract state_dict for VQVAE
						_UpperCAmelCase      =			{}
						_UpperCAmelCase      =			"""first_stage_model."""
						for key in keys:
												if key.startswith(snake_case       ):
																		_UpperCAmelCase      =			state_dict[key]

    # extract state_dict for UNetLDM
						_UpperCAmelCase      =			{}
						_UpperCAmelCase      =			"""model.diffusion_model."""
						for key in keys:
												if key.startswith(snake_case       ):
																		_UpperCAmelCase      =			state_dict[key]

						_UpperCAmelCase      =			config.model.params.first_stage_config.params
						_UpperCAmelCase      =			config.model.params.unet_config.params

						_UpperCAmelCase      =			VQModel(**snake_case       ).eval()
						vqvae.load_state_dict(snake_case       )

						_UpperCAmelCase      =			UNetLDMModel(**snake_case       ).eval()
						unet.load_state_dict(snake_case       )

						_UpperCAmelCase      =			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			,     )

						_UpperCAmelCase      =			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)





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[int]:
						if len(snake_case       ) == 0:
												return array

						_UpperCAmelCase    ,					_UpperCAmelCase      =			min(snake_case       ), max(snake_case       )

						# Compute the variables
						_UpperCAmelCase      =			_max - _min + 1
						_UpperCAmelCase    ,					_UpperCAmelCase      =			[0] * holes_range, [0] * holes_range

						# Make the sorting.
						for i in array:
												_UpperCAmelCase      =			i - _min
												_UpperCAmelCase      =			i
												holes_repeat[index] += 1

						# Makes the array back by replacing the numbers.
						_UpperCAmelCase      =			0
						for i in range(snake_case       ):
												while holes_repeat[i] > 0:
																		_UpperCAmelCase      =			holes[i]
																		index += 1
																		holes_repeat[i] -= 1

    # Returns the sorted array.
						return array


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			input("Enter numbers separated by comma:\n")
				a													=			[int(x) for x in user_input.split(",")]
				print(pigeon_sort(unsorted))