


'''simple docstring'''






from __future__ import annotations


def    _snake_case  (	A					,       A					,       A					,       A					,       A					,       )		->       None:
							lowerCAmelCase__			  =      len(A			)

							# If row is equal to the size of the board it means there are a queen in each row in
							# the current board (possible_board)
							if row == n:
														# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
														# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
														boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]			)
														return

							# We iterate each column in the row to find all possible results in each row
							for col in range(A			):
														# We apply that we learned previously. First we check that in the current board
														# (possible_board) there are not other same value because if there is it means
														# that there are a collision in vertical. Then we apply the two formulas we
														# learned before:
														#
														# 45º: y - x = b or 45: row - col = b
														# 135º: y + x = b or row + col = b.
														#
														# And we verify if the results of this two formulas not exist in their variables
														# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
														#
														# If any or these are True it means there is a collision so we continue to the
														# next value in the for loop.
														if (
														    col in possible_board
														    or row - col in diagonal_right_collisions
														    or row + col in diagonal_left_collisions
														):
																					continue

														# If it is False we call dfs function again and we update the inputs
														depth_first_search(
														    [*possible_board, col]					,       [*diagonal_right_collisions, row - col]					,       [*diagonal_left_collisions, row + col]					,       A					,       A					,       )


def    _snake_case  (	A			)		->       None:
							lowerCAmelCase__			  =      []
							depth_first_search([]					,       []					,       []					,       A					,       A			)

							# Print all the boards
							for board in boards:
														for column in board:
																					print(A			)
														print(''''''			)

							print(len(A			)					,       '''solutions were found.'''			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



'''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)