





"""simple docstring"""



from __future__ import annotations







def 		__lowercase					(       snake_case_  :  tuple[int, int]    ,snake_case_  :  int						)       ->list[tuple[int, int]]:



    '''simple docstring'''







    __A			,	__A   :						Optional[int]									=					position
    __A   :						Dict									=					[
        (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),
    ]
    __A   :						Any									=					[]

    for position in positions:
        __A			,	__A   :						str									=					position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(lowerCAmelCase__						)

    return permissible_positions







def 		__lowercase					(       snake_case_  :  list[list[int]]						)       ->bool:



    '''simple docstring'''







    return not any(elem == 0 for row in board for elem in row						)







def 		__lowercase					(       snake_case_  :  list[list[int]]    ,snake_case_  :  tuple[int, int]    ,snake_case_  :  int						)       ->bool:



    '''simple docstring'''







    if is_complete(lowerCAmelCase__						):
        return True

    for position in get_valid_pos(lowerCAmelCase__    ,len(lowerCAmelCase__						)						):
        __A			,	__A   :						List[str]									=					position

        if board[y][x] == 0:
            __A   :						Optional[int]									=					curr + 1
            if open_knight_tour_helper(lowerCAmelCase__    ,lowerCAmelCase__    ,curr + 1						):
                return True
            __A   :						Optional[int]									=					0

    return False







def 		__lowercase					(       snake_case_  :  int						)       ->list[list[int]]:



    '''simple docstring'''







    __A   :						int									=					[[0 for i in range(lowerCAmelCase__						)] for j in range(lowerCAmelCase__						)]

    for i in range(lowerCAmelCase__						):
        for j in range(lowerCAmelCase__						):
            __A   :						Dict									=					1
            if open_knight_tour_helper(lowerCAmelCase__    ,(i, j)    ,1						):
                return board
            __A   :						Optional[Any]									=					0

    __A   :						Optional[int]									=					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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_			=      logging.get_logger(__name__)

a_			=      {
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""dinat"""

 _lowerCamelCase				=					{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(   self ,						__lowerCamelCase=4 ,						__lowerCamelCase=3 ,						__lowerCamelCase=64 ,						__lowerCamelCase=[3, 4, 6, 5] ,						__lowerCamelCase=[2, 4, 8, 16] ,						__lowerCamelCase=7 ,						__lowerCamelCase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,						__lowerCamelCase=3.0 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A   :						Dict									=					patch_size
     __A   :						Union[str, Any]									=					num_channels
     __A   :						str									=					embed_dim
     __A   :						Optional[Any]									=					depths
     __A   :						int									=					len(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					num_heads
     __A   :						Tuple									=					kernel_size
     __A   :						Optional[int]									=					dilations
     __A   :						Tuple									=					mlp_ratio
     __A   :						Optional[int]									=					qkv_bias
     __A   :						int									=					hidden_dropout_prob
     __A   :						Dict									=					attention_probs_dropout_prob
     __A   :						int									=					drop_path_rate
     __A   :						Dict									=					hidden_act
     __A   :						Any									=					layer_norm_eps
     __A   :						Tuple									=					initializer_range
     # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     __A   :						str									=					int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A   :						List[Any]									=					layer_scale_init_value
     __A   :						Any									=					['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,						len(__lowerCamelCase  ) + 1  )]
     __A			,	__A   :						Union[str, Any]									=					get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase ,						out_indices=__lowerCamelCase ,						stage_names=self.stage_names  )




