





'''simple docstring'''






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import BridgeTowerImageProcessor




class  _A (					unittest.TestCase		):

		def __init__(   self					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   int = 32  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[int, float] = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Any=7  ,      __magic_name__					:   Tuple=30  ,      __magic_name__					:   List[Any]=4_00  ,      __magic_name__					:   Optional[Any]=3  ,      )					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     parent
					__snake_case					:      Optional[Any]							      =     do_resize
					__snake_case					:      List[str]							      =     size if size is not None else {"""shortest_edge""": 2_88}
					__snake_case					:      Any							      =     size_divisor
					__snake_case					:      List[str]							      =     do_rescale
					__snake_case					:      Optional[int]							      =     rescale_factor
					__snake_case					:      Any							      =     do_normalize
					__snake_case					:      Any							      =     do_center_crop
					__snake_case					:      Tuple							      =     image_mean
					__snake_case					:      Tuple							      =     image_std
					__snake_case					:      int							      =     do_pad
					__snake_case					:      Union[str, Any]							      =     batch_size
					__snake_case					:      Union[str, Any]							      =     num_channels
					__snake_case					:      Optional[int]							      =     min_resolution
					__snake_case					:      str							      =     max_resolution

		def 	lowercase__     (   self					:   List[Any]			)					->    Any:


					"""simple docstring"""

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Union[str, Any]=False			)					->    Any:


					"""simple docstring"""

					if not batched:
								__snake_case					:      Optional[Any]							      =     self.size["""shortest_edge"""]
								__snake_case					:      Optional[int]							      =     image_inputs[0]
								if isinstance(__magic_name__  ,      Image.Image			):
											__snake_case      ,     __snake_case					:      List[Any]							      =     image.size
								else:
											__snake_case      ,     __snake_case					:      List[str]							      =     image.shape[1], image.shape[2]
								__snake_case					:      Optional[Any]							      =     size / min(__magic_name__  ,      __magic_name__			)
								if h < w:
											__snake_case      ,     __snake_case					:      List[str]							      =     size, scale * w
								else:
											__snake_case      ,     __snake_case					:      int							      =     scale * h, size

								__snake_case					:      str							      =     int((13_33 / 8_00) * size			)
								if max(__magic_name__  ,      __magic_name__			) > max_size:
											__snake_case					:      Dict							      =     max_size / max(__magic_name__  ,      __magic_name__			)
											__snake_case					:      str							      =     newh * scale
											__snake_case					:      List[Any]							      =     neww * scale

								__snake_case      ,     __snake_case					:      int							      =     int(newh + 0.5			), int(neww + 0.5			)
								__snake_case      ,     __snake_case					:      Tuple							      =     (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								__snake_case					:      Tuple							      =     []
								for image in image_inputs:
											__snake_case      ,     __snake_case					:      Tuple							      =     self.get_expected_values([image]			)
											expected_values.append((expected_height, expected_width)			)
								__snake_case					:      Dict							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[0]			)[0]
								__snake_case					:      Union[str, Any]							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[1]			)[1]

					return expected_height, expected_width





@require_torch
@require_vision
class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Any				     =   BridgeTowerImageProcessor if is_vision_available() else None

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     BridgeTowerImageProcessingTester(self			)

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Any:


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()

		def 	lowercase__     (   self					:   int			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_mean"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_std"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_normalize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_resize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size_divisor"""			)			)

		def 	lowercase__     (   self					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Dict			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      Image.Image			)

					# Test not batched input
					__snake_case					:      Any							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      int							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Dict							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					__snake_case					:      str							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      numpify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      np.ndarray			)

					# Test not batched input
					__snake_case					:      int							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Any							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Tuple							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__snake_case					:      Dict							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      torchify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      torch.Tensor			)

					# Test not batched input
					__snake_case					:      str							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Optional[int]							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )









from __future__ import annotations






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

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   len(UpperCamelCase__					)

						# 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(UpperCamelCase__					):
												# 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]       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						depth_first_search([]       ,			[]       ,			[]       ,			UpperCamelCase__       ,			UpperCamelCase__					)

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							n_queens_solution(4)



