
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_A   =			0

_A   =			[
    [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],
]

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

_A   =			tuple[int, int]







class 					lowerCamelCase		:





     def __init__(self				:   Optional[Any]    ,   _A				:   int    ,   _A				:   int    ,   _A				:   int    ,   _A				:   int    ,   _A				:   int    ,   _A				:   Node | None    ,   )						->						None:
         snake_case								= pos_x
         snake_case								= pos_y
         snake_case								= (pos_y, pos_x)
         snake_case								= goal_x
         snake_case								= goal_y
         snake_case								= g_cost
         snake_case								= parent
         snake_case								= self.calculate_heuristic()
         snake_case								= self.g_cost + self.h_cost





     def 		UpperCAmelCase(self				:   Any       )						->						float:
         snake_case								= self.pos_x - self.goal_x
         snake_case								= self.pos_y - self.goal_y
         if HEURISTIC == 1:
             return abs(_A       ) + abs(_A       )
         else:
             return sqrt(dy**2 + dx**2       )





     def __lt__(self				:   Dict    ,   _A				:   Node       )						->						bool:
         return self.f_cost < other.f_cost







class 					lowerCamelCase		:





     def __init__(self				:   str    ,   _A				:   TPosition    ,   _A				:   TPosition       )						->						str:
         snake_case								= Node(start[1]    ,   start[0]    ,   goal[1]    ,   goal[0]    ,   0    ,   _A       )
         snake_case								= Node(goal[1]    ,   goal[0]    ,   goal[1]    ,   goal[0]    ,   9_9_9_9_9    ,   _A       )

         snake_case								= [self.start]
         snake_case								= []

         snake_case								= False





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

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

             self.closed_nodes.append(_A       )
             snake_case								= 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
                     snake_case								= 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       )

         return [self.start.pos]





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Node       )						->						list[Node]:
         snake_case								= []
         for action in delta:
             snake_case								= parent.pos_x + action[1]
             snake_case								= 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 		UpperCAmelCase(self				:   Dict    ,   _A				:   Node | None       )						->						list[TPosition]:
         snake_case								= node
         snake_case								= []
         while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x)       )
             snake_case								= current_node.parent
         path.reverse()
         return path







class 					lowerCamelCase		:





     def __init__(self				:   int    ,   _A				:   TPosition    ,   _A				:   TPosition       )						->						None:
         snake_case								= AStar(_A    ,   _A       )
         snake_case								= AStar(_A    ,   _A       )
         snake_case								= False





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						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()
             snake_case								= self.fwd_astar.open_nodes.pop(0       )
             snake_case								= self.bwd_astar.open_nodes.pop(0       )

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

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

             snake_case								= current_bwd_node
             snake_case								= current_fwd_node

             snake_case								= {
                 self.fwd_astar: self.fwd_astar.get_successors(_A       ),
                 self.bwd_astar: self.bwd_astar.get_successors(_A       ),
             }

             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(_A       )
                     else:
                         # retrieve the best current path
                         snake_case								= astar.open_nodes.pop(
                             astar.open_nodes.index(_A       )       )

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

         return [self.fwd_astar.start.pos]





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Node    ,   _A				:   Node       )						->						list[TPosition]:
         snake_case								= self.fwd_astar.retrace_path(_A       )
         snake_case								= self.bwd_astar.retrace_path(_A       )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case								= fwd_path + bwd_path
         return path


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

   _A   =			time.time()
   _A   =			AStar(init, goal)
   _A   =			a_star.search()
   _A   =			time.time() - start_time
   print(f"AStar execution time = {end_time:f} seconds")

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



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

