

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__	   = {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					_A			):
			"""simple docstring"""







			__lowerCamelCase        =       'mask2former'
			__lowerCamelCase        =       ['swin']
			__lowerCamelCase        =       {'hidden_size': 'hidden_dim'}


			def __init__(    self      ,      lowercase = None      ,      lowercase = 256      ,      lowercase = 256      ,      lowercase = 256      ,      lowercase = 1024      ,      lowercase = "relu"      ,      lowercase = 6      ,      lowercase = 10      ,      lowercase = 8      ,      lowercase = 0.0      ,      lowercase = 2048      ,      lowercase = False      ,      lowercase = False      ,      lowercase = 4      ,      lowercase = 255      ,      lowercase = 100      ,      lowercase = 0.1      ,      lowercase = 2.0      ,      lowercase = 5.0      ,      lowercase = 5.0      ,      lowercase = 12544      ,      lowercase = 3.0      ,      lowercase = 0.75      ,      lowercase = 0.02      ,      lowercase = 1.0      ,      lowercase = True      ,      lowercase = [4, 8, 16, 32]      ,      lowercase = None      ,      **lowercase      ,      )  ->						List[str]:
							'''simple docstring'''






							if backbone_config is None:
											logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."       )
											A__        =			CONFIG_MAPPING["swin"](
											    image_size=224      ,      in_channels=3      ,      patch_size=4      ,      embed_dim=96      ,      depths=[2, 2, 18, 2]      ,      num_heads=[3, 6, 12, 24]      ,      window_size=7      ,      drop_path_rate=0.3      ,      use_absolute_embeddings=__lowerCamelCase      ,      out_features=["stage1", "stage2", "stage3", "stage4"]      ,      )

							if isinstance(__lowerCamelCase      ,      __lowerCamelCase       ):
											A__        =			backbone_config.pop("model_type"       )
											A__        =			CONFIG_MAPPING[backbone_model_type]
											A__        =			config_class.from_dict(__lowerCamelCase       )

							# verify that the backbone is supported
							if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
											    F'Supported model types: {",".join(self.backbones_supported       )}'       )

							A__        =			backbone_config
							A__        =			feature_size
							A__        =			mask_feature_size
							A__        =			hidden_dim
							A__        =			encoder_feedforward_dim
							A__        =			activation_function
							A__        =			encoder_layers
							A__        =			decoder_layers
							A__        =			num_attention_heads
							A__        =			dropout
							A__        =			dim_feedforward
							A__        =			pre_norm
							A__        =			enforce_input_projection
							A__        =			common_stride
							A__        =			ignore_value
							A__        =			num_queries
							A__        =			no_object_weight
							A__        =			class_weight
							A__        =			mask_weight
							A__        =			dice_weight
							A__        =			train_num_points
							A__        =			oversample_ratio
							A__        =			importance_sample_ratio
							A__        =			init_std
							A__        =			init_xavier_std
							A__        =			use_auxiliary_loss
							A__        =			feature_strides
							A__        =			output_auxiliary_logits
							A__        =			decoder_layers

							super().__init__(**__lowerCamelCase       )


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      **lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							return cls(
							    backbone_config=__lowerCamelCase      ,      **__lowerCamelCase      ,      )


			def      UpperCamelCase		(    self       )  ->						Dict[str, any]:
							'''simple docstring'''






							A__        =			copy.deepcopy(self.__dict__       )
							A__        =			self.backbone_config.to_dict()
							A__        =			self.__class__.model_type
							return output









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# 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(SCREAMING_SNAKE_CASE_      ):
								# 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]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

