






from __future__ import annotations

__A      =				[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			):

					'''simple docstring'''







					_A								= [
					    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
					]  # the reference grid
					_A								= 1
					_A								= [
					    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowercase ) )
					]  # the action grid

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

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

					while not found and not resign:
										if len(_lowercase ) == 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()
															_A								= cell.pop()
															_A								= next_cell[2]
															_A								= next_cell[3]
															_A								= next_cell[1]

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

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


if __name__ == "__main__":
				__A      =				[
				    [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],
				]

				__A      =				[0, 0]
				# all coordinates are given in format [y,x]
				__A      =				[len(grid) - 1, len(grid[0]) - 1]
				__A      =				1

				# the cost map which pushes the path closer to the goal
				__A      =				[[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])):
												__A      =				abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																__A      =				99

				__A ,  __A      =				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 collections import OrderedDict
from typing import Mapping

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


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



