





from __future__ import annotations


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase					,    lowercase							=     position
				lowercase							=     [
				    (y + 1, x + 2),
				    (y - 1, x + 2),
				    (y + 1, x - 2),
				    (y - 1, x - 2),
				    (y + 2, x + 1),
				    (y + 2, x - 1),
				    (y - 2, x + 1),
				    (y - 2, x - 1),
				]
				lowercase							=     []

				for position in positions:
								lowercase					,    lowercase							=     position
								if 0 <= y_test < n and 0 <= x_test < n:
												permissible_positions.append(UpperCamelCase__ )

				return permissible_positions


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				return not any(elem == 0 for row in board for elem in row )


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ ):

				if is_complete(UpperCamelCase__ ):
								return True

				for position in get_valid_pos(UpperCamelCase__       ,len(UpperCamelCase__ ) ):
								lowercase					,    lowercase							=     position

								if board[y][x] == 0:
												lowercase							=     curr + 1
												if open_knight_tour_helper(UpperCamelCase__       ,UpperCamelCase__       ,curr + 1 ):
																return True
												lowercase							=     0

				return False


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				lowercase							=     [[0 for i in range(UpperCamelCase__ )] for j in range(UpperCamelCase__ )]

				for i in range(UpperCamelCase__ ):
								for j in range(UpperCamelCase__ ):
												lowercase							=     1
												if open_knight_tour_helper(UpperCamelCase__       ,(i, j)       ,1 ):
																return board
												lowercase							=     0

				lowercase							=     f"""Open Kight Tour cannot be performed on a board of size {n}"""
				raise ValueError(UpperCamelCase__ )


if __name__ == "__main__":
					import doctest

					doctest.testmod()









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""encoder"""		)
									SCREAMING_SNAKE_CASE__								=   encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""decoder"""		)
									SCREAMING_SNAKE_CASE__								=   decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   True

									return cls(encoder=encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__								=   self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.__class__.model_type
									return output