
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase						:      Any									= logging.get_logger(__name__)

lowercase						:      Dict									= {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  "beit"







	def __init__(      self	:List[Any]  ,     a	:str=8_1_9_2  ,     a	:Dict=7_6_8  ,     a	:int=1_2  ,     a	:Optional[Any]=1_2  ,     a	:Optional[int]=3_0_7_2  ,     a	:Dict="gelu"  ,     a	:Optional[int]=0.0  ,     a	:Optional[int]=0.0  ,     a	:Optional[Any]=0.02  ,     a	:Union[str, Any]=1E-1_2  ,     a	:Optional[Any]=2_2_4  ,     a	:Union[str, Any]=1_6  ,     a	:str=3  ,     a	:str=False  ,     a	:str=False  ,     a	:int=False  ,     a	:List[str]=False  ,     a	:Dict=0.1  ,     a	:str=0.1  ,     a	:int=True  ,     a	:Any=[3, 5, 7, 1_1]  ,     a	:Union[str, Any]=[1, 2, 3, 6]  ,     a	:List[str]=True  ,     a	:str=0.4  ,     a	:Optional[Any]=2_5_6  ,     a	:Optional[Any]=1  ,     a	:Any=False  ,     a	:Any=2_5_5  ,     **a	:Optional[Any]  ,     )	->				Optional[Any]:
								super().__init__(**a		)

								__UpperCamelCase			:     Optional[Any]         =      vocab_size
								__UpperCamelCase			:     Optional[Any]         =      hidden_size
								__UpperCamelCase			:     Union[str, Any]         =      num_hidden_layers
								__UpperCamelCase			:     List[str]         =      num_attention_heads
								__UpperCamelCase			:     Optional[int]         =      intermediate_size
								__UpperCamelCase			:     Optional[int]         =      hidden_act
								__UpperCamelCase			:     Tuple         =      hidden_dropout_prob
								__UpperCamelCase			:     List[Any]         =      attention_probs_dropout_prob
								__UpperCamelCase			:     int         =      initializer_range
								__UpperCamelCase			:     Optional[Any]         =      layer_norm_eps

								__UpperCamelCase			:     Tuple         =      image_size
								__UpperCamelCase			:     str         =      patch_size
								__UpperCamelCase			:     int         =      num_channels
								__UpperCamelCase			:     int         =      use_mask_token
								__UpperCamelCase			:     Optional[int]         =      use_absolute_position_embeddings
								__UpperCamelCase			:     Optional[Any]         =      use_relative_position_bias
								__UpperCamelCase			:     int         =      use_shared_relative_position_bias
								__UpperCamelCase			:     Tuple         =      layer_scale_init_value
								__UpperCamelCase			:     Optional[int]         =      drop_path_rate
								__UpperCamelCase			:     Tuple         =      use_mean_pooling
								# decode head attributes (semantic segmentation)
								__UpperCamelCase			:     Dict         =      out_indices
								__UpperCamelCase			:     Any         =      pool_scales
								# auxiliary head attributes (semantic segmentation)
								__UpperCamelCase			:     Optional[int]         =      use_auxiliary_head
								__UpperCamelCase			:     Optional[int]         =      auxiliary_loss_weight
								__UpperCamelCase			:     List[Any]         =      auxiliary_channels
								__UpperCamelCase			:     Tuple         =      auxiliary_num_convs
								__UpperCamelCase			:     List[str]         =      auxiliary_concat_input
								__UpperCamelCase			:     Union[str, Any]         =      semantic_loss_ignore_index




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  version.parse('1.11')







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]		)







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				float:
								return 1E-4

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      Optional[int]									= [
    [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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







	def __lt__(      self	:Union[str, Any]  ,     a	:Dict		)	->				bool:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

															if current_node.pos == self.target.pos:
																						__UpperCamelCase			:     Dict         =      True
																						return self.retrace_path(a		)

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, Any]         =      self.get_successors(a		)

															for child_node in successors:
																						if child_node in self.closed_nodes:
																													continue

																						if child_node not in self.open_nodes:
																													self.open_nodes.append(a		)
																						else:
																													# retrieve the best current path
																													__UpperCamelCase			:     Union[str, Any]         =      self.open_nodes.pop(self.open_nodes.index(a		)		)

																													if child_node.g_cost < better_node.g_cost:
																																				self.open_nodes.append(a		)
																													else:
																																				self.open_nodes.append(a		)

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







	def      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      parent.pos_x + action[1]
															__UpperCamelCase			:     List[Any]         =      parent.pos_y + action[0]

															if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(a		) - 1):
																						continue

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

															successors.append(
															    Node(
															        a  ,     a  ,     self.target.pos_y  ,     self.target.pos_x  ,     parent.g_cost + 1  ,     a  ,     )		)
								return successors







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
		lowercase						:      List[str]									= (0, 0)
		lowercase						:      int									= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		print('------')

		lowercase						:      Any									= GreedyBestFirst(init, goal)
		lowercase						:      List[str]									= greedy_bf.search()
		if path:
				for pos_x, pos_y in path:
						lowercase						:      Optional[int]									= 2

				for elem in grid:
						print(elem)