


from __future__ import annotations

snake_case_						:			int													=							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__	,):
				_SCREAMING_SNAKE_CASE			  =[
				    [0 for col in range(len(grid[0]))] for row in range(len(a__))
				]  # the reference grid
				_SCREAMING_SNAKE_CASE			  =1
				_SCREAMING_SNAKE_CASE			  =[
				    [0 for col in range(len(grid[0]))] for row in range(len(a__))
				]  # the action grid

				_SCREAMING_SNAKE_CASE			  =init[0]
				_SCREAMING_SNAKE_CASE			  =init[1]
				_SCREAMING_SNAKE_CASE			  =0
				_SCREAMING_SNAKE_CASE			  =g + heuristic[x][y]  # cost from starting cell to destination cell
				_SCREAMING_SNAKE_CASE			  =[[f, g, x, y]]

				_SCREAMING_SNAKE_CASE			  =False  # flag that is set when search is complete
				_SCREAMING_SNAKE_CASE			  =False  # flag set if we can't find expand

				while not found and not resign:
								if len(a__) == 0:
												raise ValueError('''Algorithm is unable to find solution''')
								else:  # to choose the least costliest action so as to move closer to the goal
												cell.sort()
												cell.reverse()
												_SCREAMING_SNAKE_CASE			  =cell.pop()
												_SCREAMING_SNAKE_CASE			  =next_cell[2]
												_SCREAMING_SNAKE_CASE			  =next_cell[3]
												_SCREAMING_SNAKE_CASE			  =next_cell[1]

												if x == goal[0] and y == goal[1]:
																_SCREAMING_SNAKE_CASE			  =True
												else:
																for i in range(len(a__)):  # to try out different valid actions
																				_SCREAMING_SNAKE_CASE			  =x + DIRECTIONS[i][0]
																				_SCREAMING_SNAKE_CASE			  =y + DIRECTIONS[i][1]
																				if xa >= 0 and xa < len(a__) and ya >= 0 and ya < len(grid[0]):
																								if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																												_SCREAMING_SNAKE_CASE			  =g + cost
																												_SCREAMING_SNAKE_CASE			  =ga + heuristic[xa][ya]
																												cell.append([fa, ga, xa, ya])
																												_SCREAMING_SNAKE_CASE			  =1
																												_SCREAMING_SNAKE_CASE			  =i
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =goal[0]
				_SCREAMING_SNAKE_CASE			  =goal[1]
				invpath.append([x, y])  # we get the reverse path from here
				while x != init[0] or y != init[1]:
								_SCREAMING_SNAKE_CASE			  =x - DIRECTIONS[action[x][y]][0]
								_SCREAMING_SNAKE_CASE			  =y - DIRECTIONS[action[x][y]][1]
								_SCREAMING_SNAKE_CASE			  =xa
								_SCREAMING_SNAKE_CASE			  =ya
								invpath.append([x, y])

				_SCREAMING_SNAKE_CASE			  =[]
				for i in range(len(a__)):
								path.append(invpath[len(a__) - 1 - i])
				return path, action


if __name__ == "__main__":
							snake_case_						:			Dict													=							[
							    [0, 1, 0, 0, 0, 0],
							    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
							    [0, 1, 0, 0, 0, 0],
							    [0, 1, 0, 0, 1, 0],
							    [0, 0, 0, 0, 1, 0],
							]

							snake_case_						:			Union[str, Any]													=							[0, 0]
							# all coordinates are given in format [y,x]
							snake_case_						:			Dict													=							[len(grid) - 1, len(grid[0]) - 1]
							snake_case_						:			List[Any]													=							1

							# the cost map which pushes the path closer to the goal
							snake_case_						:			Dict													=							[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
							for i in range(len(grid)):
														for j in range(len(grid[0])):
																					snake_case_						:			Dict													=							abs(i - goal[0]) + abs(j - goal[1])
																					if grid[i][j] == 1:
																												# added extra penalty in the heuristic map
																												snake_case_						:			Optional[Any]													=							99

							snake_case_						,				snake_case_						:			Optional[Any]													=							search(grid, init, goal, cost, heuristic)

							print('''ACTION MAP''')
							for i in range(len(action)):
														print(action[i])

							for i in range(len(path)):
														print(path[i])



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


snake_case_						:			int													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"lilt"




						def __init__(       self					:      int				,    _a					:      Dict=3_0522				,    _a					:      str=768				,    _a					:      Optional[int]=12				,    _a					:      str=12				,    _a					:      List[str]=3072				,    _a					:      Any="gelu"				,    _a					:      int=0.1				,    _a					:      Any=0.1				,    _a					:      Union[str, Any]=512				,    _a					:      str=2				,    _a					:      List[Any]=0.02				,    _a					:      str=1E-12				,    _a					:      str=0				,    _a					:      str="absolute"				,    _a					:      Any=None				,    _a					:      Dict=4				,    _a					:      int=1024				,    **_a					:      List[Any]				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =classifier_dropout
										_SCREAMING_SNAKE_CASE			  =channel_shrink_ratio
										_SCREAMING_SNAKE_CASE			  =max_ad_position_embeddings