"""simple docstring"""





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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class  _snake_case     (					a__		):
						snake_case__			  =			"donut-swin"

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






						def __init__(       self      :			str							,					UpperCAmelCase      :			List[str]=224							,					UpperCAmelCase      :			Tuple=4							,					UpperCAmelCase      :			List[Any]=3							,					UpperCAmelCase      :			Union[str, Any]=96							,					UpperCAmelCase      :			Dict=[2, 2, 6, 2]							,					UpperCAmelCase      :			str=[3, 6, 12, 24]							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			List[Any]=4.0							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Tuple=0.0							,					UpperCAmelCase      :			List[Any]=0.0							,					UpperCAmelCase      :			Optional[Any]=0.1							,					UpperCAmelCase      :			Union[str, Any]="gelu"							,					UpperCAmelCase      :			Optional[Any]=False							,					UpperCAmelCase      :			Any=0.0_2							,					UpperCAmelCase      :			Union[str, Any]=1E-5							,					**UpperCAmelCase      :			List[str]							,					):
													super().__init__(**UpperCAmelCase    )

													__lowerCamelCase					:		Any		   =     image_size
													__lowerCamelCase					:		Union[str, Any]		   =     patch_size
													__lowerCamelCase					:		Optional[int]		   =     num_channels
													__lowerCamelCase					:		int		   =     embed_dim
													__lowerCamelCase					:		int		   =     depths
													__lowerCamelCase					:		Tuple		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     num_heads
													__lowerCamelCase					:		Tuple		   =     window_size
													__lowerCamelCase					:		int		   =     mlp_ratio
													__lowerCamelCase					:		Optional[Any]		   =     qkv_bias
													__lowerCamelCase					:		Tuple		   =     hidden_dropout_prob
													__lowerCamelCase					:		Union[str, Any]		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		Optional[int]		   =     drop_path_rate
													__lowerCamelCase					:		Optional[Any]		   =     hidden_act
													__lowerCamelCase					:		Dict		   =     use_absolute_embeddings
													__lowerCamelCase					:		int		   =     layer_norm_eps
													__lowerCamelCase					:		Optional[int]		   =     initializer_range
													# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
													# this indicates the channel dimension after the last stage of the model
													__lowerCamelCase					:		int		   =     int(embed_dim * 2 ** (len(UpperCAmelCase    ) - 1)    )
"""simple docstring"""





from __future__ import annotations


def    lowercase_   (   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     len(_lowerCamelCase      )
							# We need to create solution object to save path.
							__lowerCamelCase					:		List[Any]		   =     [[0 for _ in range(_lowerCamelCase      )] for _ in range(_lowerCamelCase      )]
							__lowerCamelCase					:		Dict		   =     run_maze(_lowerCamelCase						,   0						,   0						,   _lowerCamelCase      )
							if solved:
														print("\n".join(str(_lowerCamelCase      ) for row in solutions      )      )
							else:
														print("No solution exists!"      )
							return solved


def    lowercase_   (   _lowerCamelCase:	list[list[int]]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	int						,   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     len(_lowerCamelCase      )
							# Final check point.
							if i == j == (size - 1):
														__lowerCamelCase					:		Optional[Any]		   =     1
														return True

							__lowerCamelCase					:		int		   =     (not i < 0) and (not j < 0)  # Check lower bounds
							__lowerCamelCase					:		Union[str, Any]		   =     (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__lowerCamelCase					:		List[Any]		   =     (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__lowerCamelCase					:		Dict		   =     1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,   i + 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j + 1						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   i - 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j - 1						,   _lowerCamelCase      )
																					):
																												return True

																					__lowerCamelCase					:		List[str]		   =     0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()