

from __future__ import annotations





def   lowerCamelCase__       (       UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    list[list[str]]    ,      UpperCamelCase__     :    int    ,      )     ->	None:



					'''simple docstring'''




					_snake_case													=			len(lowercase__  )

					# 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(lowercase__  ):
										# 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]    ,      lowercase__    ,      lowercase__    ,      )





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	None:



					'''simple docstring'''




					_snake_case													=			[]
					depth_first_search([]    ,      []    ,      []    ,      lowercase__    ,      lowercase__  )

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

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

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

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			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__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


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)



