

'''simple docstring'''

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
SCREAMING_SNAKE_CASE							:				int     	=			0

SCREAMING_SNAKE_CASE							:				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],
]

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

SCREAMING_SNAKE_CASE							:				Optional[int]     	=			tuple[int, int]






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							None:
											SCREAMING_SNAKE_CASE_				=				pos_x
											SCREAMING_SNAKE_CASE_				=				pos_y
											SCREAMING_SNAKE_CASE_				=				(pos_y, pos_x)
											SCREAMING_SNAKE_CASE_				=				goal_x
											SCREAMING_SNAKE_CASE_				=				goal_y
											SCREAMING_SNAKE_CASE_				=				g_cost
											SCREAMING_SNAKE_CASE_				=				parent
											SCREAMING_SNAKE_CASE_				=				self.calculate_heuristic()
											SCREAMING_SNAKE_CASE_				=				self.g_cost + self.h_cost



				def   a__					(    self       )     ->							float:
											SCREAMING_SNAKE_CASE_				=				self.pos_x - self.goal_x
											SCREAMING_SNAKE_CASE_				=				self.pos_y - self.goal_y
											if HEURISTIC == 1:
																		return abs(_lowercase       ) + abs(_lowercase       )
											else:
																		return sqrt(dy**2 + dx**2       )





				def __lt__(    self,     _lowercase       )     ->							bool:
											return self.f_cost < other.f_cost






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				Node(start[1],     start[0],     goal[1],     goal[0],     0,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				Node(goal[1],     goal[0],     goal[1],     goal[0],     99999,     _lowercase       )

											SCREAMING_SNAKE_CASE_				=				[self.start]
											SCREAMING_SNAKE_CASE_				=				[]

											SCREAMING_SNAKE_CASE_				=				False



				def   a__					(    self       )     ->							list[TPosition]:
											while self.open_nodes:
																		# Open Nodes are sorted using __lt__
																		self.open_nodes.sort()
																		SCREAMING_SNAKE_CASE_				=				self.open_nodes.pop(0       )

																		if current_node.pos == self.target.pos:
																									return self.retrace_path(_lowercase       )

																		self.closed_nodes.append(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				self.get_successors(_lowercase       )

																		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(_lowercase       )
																									else:
																																# retrieve the best current path
																																SCREAMING_SNAKE_CASE_				=				self.open_nodes.pop(self.open_nodes.index(_lowercase       )       )

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

											return [self.start.pos]



				def   a__					(    self,     _lowercase       )     ->							list[Node]:
											SCREAMING_SNAKE_CASE_				=				[]
											for action in delta:
																		SCREAMING_SNAKE_CASE_				=				parent.pos_x + action[1]
																		SCREAMING_SNAKE_CASE_				=				parent.pos_y + action[0]
																		if not (0 <= pos_x <= len(grid[0]       ) - 1 and 0 <= pos_y <= len(_lowercase       ) - 1):
																									continue

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

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





				def   a__					(    self,     _lowercase       )     ->							list[TPosition]:
											SCREAMING_SNAKE_CASE_				=				node
											SCREAMING_SNAKE_CASE_				=				[]
											while current_node is not None:
																		path.append((current_node.pos_y, current_node.pos_x)       )
																		SCREAMING_SNAKE_CASE_				=				current_node.parent
											path.reverse()
											return path






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase       )     ->							None:
											SCREAMING_SNAKE_CASE_				=				AStar(_lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				AStar(_lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				False



				def   a__					(    self       )     ->							list[TPosition]:
											while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																		self.fwd_astar.open_nodes.sort()
																		self.bwd_astar.open_nodes.sort()
																		SCREAMING_SNAKE_CASE_				=				self.fwd_astar.open_nodes.pop(0       )
																		SCREAMING_SNAKE_CASE_				=				self.bwd_astar.open_nodes.pop(0       )

																		if current_bwd_node.pos == current_fwd_node.pos:
																									return self.retrace_bidirectional_path(
																									    _lowercase,     _lowercase       )

																		self.fwd_astar.closed_nodes.append(_lowercase       )
																		self.bwd_astar.closed_nodes.append(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				current_bwd_node
																		SCREAMING_SNAKE_CASE_				=				current_fwd_node

																		SCREAMING_SNAKE_CASE_				=				{
																		    self.fwd_astar: self.fwd_astar.get_successors(_lowercase       ),
																		    self.bwd_astar: self.bwd_astar.get_successors(_lowercase       ),
																		}

																		for astar in [self.fwd_astar, self.bwd_astar]:
																									for child_node in successors[astar]:
																																if child_node in astar.closed_nodes:
																																							continue

																																if child_node not in astar.open_nodes:
																																							astar.open_nodes.append(_lowercase       )
																																else:
																																							# retrieve the best current path
																																							SCREAMING_SNAKE_CASE_				=				astar.open_nodes.pop(
																																							    astar.open_nodes.index(_lowercase       )       )

																																							if child_node.g_cost < better_node.g_cost:
																																														astar.open_nodes.append(_lowercase       )
																																							else:
																																														astar.open_nodes.append(_lowercase       )

											return [self.fwd_astar.start.pos]





				def   a__					(    self,     _lowercase,     _lowercase       )     ->							list[TPosition]:
											SCREAMING_SNAKE_CASE_				=				self.fwd_astar.retrace_path(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.bwd_astar.retrace_path(_lowercase       )
											bwd_path.pop()
											bwd_path.reverse()
											SCREAMING_SNAKE_CASE_				=				fwd_path + bwd_path
											return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			(0, 0)
							SCREAMING_SNAKE_CASE							:				Tuple     	=			(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			time.time()
							SCREAMING_SNAKE_CASE							:				int     	=			AStar(init, goal)
							SCREAMING_SNAKE_CASE							:				List[str]     	=			a_star.search()
							SCREAMING_SNAKE_CASE							:				Any     	=			time.time() - start_time
							print(f"AStar execution time = {end_time:f} seconds")

							SCREAMING_SNAKE_CASE							:				str     	=			time.time()
							SCREAMING_SNAKE_CASE							:				int     	=			BidirectionalAStar(init, goal)
							SCREAMING_SNAKE_CASE							:				int     	=			time.time() - bd_start_time
							print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "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    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											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_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



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





