


'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				[]


def    _snake_case  (	A					,       A					,       A			)		->       bool:
							for i in range(len(A			)			):
														if board[row][i] == 1:
																					return False
							for i in range(len(A			)			):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(A					,       -1					,       -1			)					,       range(A					,       -1					,       -1			)			):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(A					,       -1					,       -1			)					,       range(A					,       len(A			)			)			):
														if board[i][j] == 1:
																					return False
							return True


def    _snake_case  (	A					,       A			)		->       bool:
							if row >= len(A			):
														solution.append(A			)
														printboard(A			)
														print()
														return True
							for i in range(len(A			)			):
														if is_safe(A					,       A					,       A			):
																					lowerCAmelCase__			  =      1
																					solve(A					,       row + 1			)
																					lowerCAmelCase__			  =      0
							return False


def    _snake_case  (	A			)		->       None:
							for i in range(len(A			)			):
														for j in range(len(A			)			):
																					if board[i][j] == 1:
																												print('''Q'''					,       end=''' '''			)
																					else:
																												print('''.'''					,       end=''' '''			)
														print()


# n=int(input("The no. of queens"))
__UpperCAmelCase       							=				8
__UpperCAmelCase       							=				[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print('''The total no. of solutions are :''', len(solution))



'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

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

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

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      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=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


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

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