

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Any):
								torch.manual_seed(0)
								__lowerCamelCase    :    Any 	=			UNetaDModel(
								    block_out_channels=(3_2, 6_4)       ,layers_per_block=2       ,sample_size=3_2       ,in_channels=3       ,out_channels=3       ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,)
								return model
							@property
							def    lowerCAmelCase					(		self		:      List[str]):
								torch.manual_seed(0)
								__lowerCamelCase    :    Dict 	=			VQModel(
								    block_out_channels=[3_2, 6_4]       ,in_channels=3       ,out_channels=3       ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,latent_channels=3       ,)
								return model
							@property
							def    lowerCAmelCase					(		self		:      Tuple):
								torch.manual_seed(0)
								__lowerCamelCase    :    str 	=			CLIPTextConfig(
								    bos_token_id=0       ,eos_token_id=2       ,hidden_size=3_2       ,intermediate_size=3_7       ,layer_norm_eps=1E-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1_0_0_0       ,)
								return CLIPTextModel(__snake_case)


							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Any 	=			self.dummy_uncond_unet
								__lowerCamelCase    :    List[str] 	=			DDIMScheduler()
								__lowerCamelCase    :    List[Any] 	=			self.dummy_vq_model

								__lowerCamelCase    :    int 	=			LDMPipeline(unet=__snake_case       ,vqvae=__snake_case       ,scheduler=__snake_case)
								ldm.to(__snake_case)
								ldm.set_progress_bar_config(disable=__snake_case)

								__lowerCamelCase    :    str 	=			torch.manual_seed(0)
								__lowerCamelCase    :    List[Any] 	=			ldm(generator=__snake_case       ,num_inference_steps=2       ,output_type='numpy').images

								__lowerCamelCase    :    Any 	=			torch.manual_seed(0)
								__lowerCamelCase    :    Dict 	=			ldm(generator=__snake_case       ,num_inference_steps=2       ,output_type='numpy'       ,return_dict=__snake_case)[0]

								__lowerCamelCase    :    Any 	=			image[0, -3:, -3:, -1]
								__lowerCamelCase    :    List[Any] 	=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								__lowerCamelCase    :    int 	=			np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
								__lowerCamelCase    :    Tuple 	=			1E-2 if torch_device != 'mps' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance






@slow
@require_torch
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
								ldm.to(__snake_case)
								ldm.set_progress_bar_config(disable=__snake_case)

								__lowerCamelCase    :    Tuple 	=			torch.manual_seed(0)
								__lowerCamelCase    :    List[Any] 	=			ldm(generator=__snake_case       ,num_inference_steps=5       ,output_type='numpy').images

								__lowerCamelCase    :    List[str] 	=			image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__lowerCamelCase    :    int 	=			np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
								__lowerCamelCase    :    Union[str, Any] 	=			1E-2 if torch_device != 'mps' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					None:
	__lowerCamelCase    :    int 	=			len(lowerCamelCase__      )

	# 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(lowerCamelCase__      ):
		# 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]     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    list[list[str]] 	=			[]
	depth_first_search([]     ,			[]     ,			[]     ,			lowerCamelCase__     ,			lowerCamelCase__      )

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)




