



"""simple docstring"""






from __future__ import annotations






def      snake_case__   (		__lowerCamelCase				:    tuple[int, int]							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=position
			lowerCamelCase__		:		List[str]			=[
			    (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),
			]
			lowerCamelCase__		:		List[str]			=[]

			for position in positions:
						lowerCamelCase__	,   lowerCamelCase__		:		Tuple			=position
						if 0 <= y_test < n and 0 <= x_test < n:
									permissible_positions.append(__lowerCamelCase	)

			return permissible_positions






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]	):
			"""simple docstring"""
			return not any(elem == 0 for row in board for elem in row	)






def      snake_case__   (		__lowerCamelCase				:    list[list[int]]							,		__lowerCamelCase				:    tuple[int, int]							,		__lowerCamelCase				:    int	):
			"""simple docstring"""

			if is_complete(__lowerCamelCase	):
						return True

			for position in get_valid_pos(__lowerCamelCase							,		len(__lowerCamelCase	)	):
						lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=position

						if board[y][x] == 0:
									lowerCamelCase__		:		str			=curr + 1
									if open_knight_tour_helper(__lowerCamelCase							,		__lowerCamelCase							,		curr + 1	):
												return True
									lowerCamelCase__		:		Any			=0

			return False






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[[0 for i in range(__lowerCamelCase	)] for j in range(__lowerCamelCase	)]

			for i in range(__lowerCamelCase	):
						for j in range(__lowerCamelCase	):
									lowerCamelCase__		:		Union[str, Any]			=1
									if open_knight_tour_helper(__lowerCamelCase							,		(i, j)							,		1	):
												return board
									lowerCamelCase__		:		str			=0

			lowerCamelCase__		:		List[str]			=f'''Open Kight Tour cannot be performed on a board of size {n}'''
			raise ValueError(__lowerCamelCase	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




