



'''simple docstring'''





from __future__ import annotations

import time

snake_case__					:     Tuple    		=   list[tuple[int, int]]

snake_case__					:     Optional[Any]    		=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

snake_case__					:     Any    		=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Node | None	):






							'''simple docstring'''



							__lowercase		       =			pos_x
							__lowercase		       =			pos_y
							__lowercase		       =			(pos_y, pos_x)
							__lowercase		       =			goal_x
							__lowercase		       =			goal_y
							__lowercase		       =			parent

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							int							,      lowerCamelCase	:							tuple[int, int]							,      lowerCamelCase	:							tuple[int, int]	):






							'''simple docstring'''



							__lowercase		       =			Node(start[1]							,      start[0]							,      goal[1]							,      goal[0]							,      lowerCamelCase	)
							__lowercase		       =			Node(goal[1]							,      goal[0]							,      goal[1]							,      goal[0]							,      lowerCamelCase	)

							__lowercase		       =			[self.start]
							__lowercase		       =			False



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							while self.node_queue:
								__lowercase		       =			self.node_queue.pop(0	)

								if current_node.pos == self.target.pos:
									__lowercase		       =			True
									return self.retrace_path(lowerCamelCase	)

								__lowercase		       =			self.get_successors(lowerCamelCase	)

								for node in successors:
									self.node_queue.append(lowerCamelCase	)

							if not self.reached:
								return [self.start.pos]
							return None



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Node	):






							'''simple docstring'''



							__lowercase		       =			[]
							for action in delta:
								__lowercase		       =			parent.pos_x + action[1]
								__lowercase		       =			parent.pos_y + action[0]
								if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCamelCase	) - 1):
									continue

								if grid[pos_y][pos_x] != 0:
									continue

								successors.append(
								    Node(lowerCamelCase							,      lowerCamelCase							,      self.target.pos_y							,      self.target.pos_x							,      lowerCamelCase	)	)
							return successors



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							Node | None	):






							'''simple docstring'''



							__lowercase		       =			node
							__lowercase		       =			[]
							while current_node is not None:
								path.append((current_node.pos_y, current_node.pos_x)	)
								__lowercase		       =			current_node.parent
							path.reverse()
							return path

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Any							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			BreadthFirstSearch(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			BreadthFirstSearch(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			False



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
								__lowercase		       =			self.fwd_bfs.node_queue.pop(0	)
								__lowercase		       =			self.bwd_bfs.node_queue.pop(0	)

								if current_bwd_node.pos == current_fwd_node.pos:
									__lowercase		       =			True
									return self.retrace_bidirectional_path(
									    lowerCamelCase							,      lowerCamelCase	)

								__lowercase		       =			current_bwd_node
								__lowercase		       =			current_fwd_node

								__lowercase		       =			{
								    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCamelCase	),
								    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCamelCase	),
								}

								for bfs in [self.fwd_bfs, self.bwd_bfs]:
									for node in successors[bfs]:
										bfs.node_queue.append(lowerCamelCase	)

							if not self.reached:
								return [self.fwd_bfs.start.pos]
							return None



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Node							,      lowerCamelCase	:							Node	):






							'''simple docstring'''



							__lowercase		       =			self.fwd_bfs.retrace_path(lowerCamelCase	)
							__lowercase		       =			self.bwd_bfs.retrace_path(lowerCamelCase	)
							bwd_path.pop()
							bwd_path.reverse()
							__lowercase		       =			fwd_path + bwd_path
							return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	import doctest

	doctest.testmod()
	snake_case__					:     Dict    		=   (0, 0)
	snake_case__					:     List[Any]    		=   (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	snake_case__					:     Any    		=   time.time()
	snake_case__					:     str    		=   BreadthFirstSearch(init, goal)
	snake_case__					:     int    		=   bfs.search()
	snake_case__					:     List[str]    		=   time.time() - start_bfs_time

	print("""Unidirectional BFS computation time : """, bfs_time)

	snake_case__					:     List[Any]    		=   time.time()
	snake_case__					:     Optional[Any]    		=   BidirectionalBreadthFirstSearch(init, goal)
	snake_case__					:     Union[str, Any]    		=   bd_bfs.search()
	snake_case__					:     Union[str, Any]    		=   time.time() - start_bd_bfs_time

	print("""Bidirectional BFS computation time : """, bd_bfs_time)








from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


snake_case__					:     Any    		=   logging.get_logger(__name__)

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """maskformer-swin"""

						_snake_case			:     List[str]      							=      {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any=224							,      lowerCamelCase	:							Optional[Any]=4							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							Tuple=96							,      lowerCamelCase	:							str=[2, 2, 6, 2]							,      lowerCamelCase	:							Dict=[3, 6, 12, 24]							,      lowerCamelCase	:							Optional[Any]=7							,      lowerCamelCase	:							Any=4.0							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							Optional[int]=0.0							,      lowerCamelCase	:							List[str]=0.1							,      lowerCamelCase	:							int="gelu"							,      lowerCamelCase	:							Optional[int]=False							,      lowerCamelCase	:							List[Any]=0.02							,      lowerCamelCase	:							Tuple=1e-5							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=None							,      **lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			embed_dim
							__lowercase		       =			depths
							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			num_heads
							__lowercase		       =			window_size
							__lowercase		       =			mlp_ratio
							__lowercase		       =			qkv_bias
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			drop_path_rate
							__lowercase		       =			hidden_act
							__lowercase		       =			use_absolute_embeddings
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			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
							__lowercase		       =			int(embed_dim * 2 ** (len(lowerCamelCase	) - 1)	)
							__lowercase		       =			["stem"] + [f"""stage{idx}""" for idx in range(1							,      len(lowerCamelCase	) + 1	)]
							__lowercase       ,    __lowercase		       =			get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase							,      out_indices=lowerCamelCase							,      stage_names=self.stage_names	)



