
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase    :   Any		       =				0

lowercase    :   Any		       =				[
    [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    :   str		       =				[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase    :   int		       =				tuple[int, int]

class __snake_case						:
    def __init__(    self						,snake_case						,snake_case						,snake_case						,snake_case						,snake_case						,snake_case						,):



           '''simple docstring'''







           lowercase				:	List[Any]			      = pos_x
           lowercase				:	Tuple			      = pos_y
           lowercase				:	Dict			      = (pos_y, pos_x)
           lowercase				:	Optional[Any]			      = goal_x
           lowercase				:	Tuple			      = goal_y
           lowercase				:	Optional[int]			      = g_cost
           lowercase				:	Dict			      = parent
           lowercase				:	Union[str, Any]			      = self.calculate_heuristic()
           lowercase				:	str			      = self.g_cost + self.h_cost
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	int			      = self.pos_x - self.goal_x
           lowercase				:	Optional[Any]			      = self.pos_y - self.goal_y
           if HEURISTIC == 1:
                  return abs(snake_case_ ) + abs(snake_case_ )
           else:
                  return sqrt(dy**2 + dx**2 )
    def __lt__(    self						,snake_case ):



           '''simple docstring'''







           return self.f_cost < other.f_cost






class __snake_case						:
    def __init__(    self						,snake_case						,snake_case ):



           '''simple docstring'''







           lowercase				:	Optional[int]			      = Node(start[1]						,start[0]						,goal[1]						,goal[0]						,0						,snake_case_ )
           lowercase				:	Optional[Any]			      = Node(goal[1]						,goal[0]						,goal[1]						,goal[0]						,99999						,snake_case_ )

           lowercase				:	Tuple			      = [self.start]
           lowercase				:	Tuple			      = []

           lowercase				:	List[Any]			      = False
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           while self.open_nodes:
                  # Open Nodes are sorted using __lt__
                  self.open_nodes.sort()
                  lowercase				:	Optional[int]			      = self.open_nodes.pop(0 )

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

                  self.closed_nodes.append(snake_case_ )
                  lowercase				:	List[str]			      = self.get_successors(snake_case_ )

                  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(snake_case_ )
                         else:
                                # retrieve the best current path
                                lowercase				:	Optional[int]			      = self.open_nodes.pop(self.open_nodes.index(snake_case_ ) )

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

           return [self.start.pos]
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Any			      = []
           for action in delta:
                  lowercase				:	Optional[Any]			      = parent.pos_x + action[1]
                  lowercase				:	str			      = parent.pos_y + action[0]
                  if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case_ ) - 1):
                         continue

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

                  successors.append(
                      Node(
                          snake_case_						,snake_case_						,self.target.pos_y						,self.target.pos_x						,parent.g_cost + 1						,snake_case_						,) )
           return successors
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Dict			      = node
           lowercase				:	Union[str, Any]			      = []
           while current_node is not None:
                  path.append((current_node.pos_y, current_node.pos_x) )
                  lowercase				:	int			      = current_node.parent
           path.reverse()
           return path






class __snake_case						:
    def __init__(    self						,snake_case						,snake_case ):



           '''simple docstring'''







           lowercase				:	List[str]			      = AStar(snake_case_						,snake_case_ )
           lowercase				:	Optional[Any]			      = AStar(snake_case_						,snake_case_ )
           lowercase				:	Union[str, Any]			      = False
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                  self.fwd_astar.open_nodes.sort()
                  self.bwd_astar.open_nodes.sort()
                  lowercase				:	Optional[int]			      = self.fwd_astar.open_nodes.pop(0 )
                  lowercase				:	Optional[int]			      = self.bwd_astar.open_nodes.pop(0 )

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

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

                  lowercase				:	Dict			      = current_bwd_node
                  lowercase				:	Union[str, Any]			      = current_fwd_node

                  lowercase				:	Tuple			      = {
                      self.fwd_astar: self.fwd_astar.get_successors(snake_case_ ),
                      self.bwd_astar: self.bwd_astar.get_successors(snake_case_ ),
                  }

                  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(snake_case_ )
                                else:
                                       # retrieve the best current path
                                       lowercase				:	Dict			      = astar.open_nodes.pop(
                                           astar.open_nodes.index(snake_case_ ) )

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

           return [self.fwd_astar.start.pos]
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case ):



           '''simple docstring'''







           lowercase				:	Union[str, Any]			      = self.fwd_astar.retrace_path(snake_case_ )
           lowercase				:	Dict			      = self.bwd_astar.retrace_path(snake_case_ )
           bwd_path.pop()
           bwd_path.reverse()
           lowercase				:	List[str]			      = fwd_path + bwd_path
           return path


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

  lowercase    :   Union[str, Any]		       =				time.time()
  lowercase    :   Any		       =				AStar(init, goal)
  lowercase    :   Optional[Any]		       =				a_star.search()
  lowercase    :   Dict		       =				time.time() - start_time
  print(F'''AStar execution time = {end_time:f} seconds''')

  lowercase    :   Tuple		       =				time.time()
  lowercase    :   str		       =				BidirectionalAStar(init, goal)
  lowercase    :   Union[str, Any]		       =				time.time() - bd_start_time
  print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad							,     model.parameters()				)
    _A     =							sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{val_avg_loss:.4f}-{step_count}'
    else:
        raise NotImplementedError(
            F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
            ' function.'				)

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    return EarlyStopping(
        monitor=F"val_{metric}"							,     mode='min' if 'loss' in metric else 'max'							,     patience=_SCREAMING_SNAKE_CASE							,     verbose=_SCREAMING_SNAKE_CASE							,     )







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"							)
         _A     =							trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
         # Log results
         _A     =							Path(pl_module.hparams.output_dir							)
         if type_path == "test":
             _A     =							od / 'test_results.txt'
             _A     =							od / 'test_generations.txt'
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             _A     =							od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             _A     =							od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

         if "preds" in metrics:
             _A     =							'\n'.join(metrics['preds']							)
             generations_file.open('w+'							).write(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							count_trainable_parameters(snake_case_							)
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





