





import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Dict        =      (0, 0)
        A_       :					Union[str, Any]        =      None
        A_       :					List[str]        =      0
        A_       :					int        =      0
        A_       :					List[str]        =      0



     def __eq__(				self							:Dict      ,   snake_case							:int	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Optional[Any]      ,   snake_case							:Dict=(5, 5)	):





        '''simple docstring'''




        A_       :					Any        =      np.zeros(SCREAMING_SNAKE_CASE_	)
        A_       :					List[str]        =      world_size[0]
        A_       :					str        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:Any	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A_       :					List[str]        =      cell.position[0]
        A_       :					str        =      cell.position[1]
        A_       :					List[Any]        =      []
        for n in neughbour_cord:
           A_       :					Dict        =      current_x + n[0]
           A_       :					Any        =      current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
              A_       :					Union[str, Any]        =      Cell()
              A_       :					Optional[int]        =      (x, y)
              A_       :					Optional[Any]        =      cell
              neighbours.append(SCREAMING_SNAKE_CASE_	)
        return neighbours




def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Union[str, Any]						)			->    Any:
   A_       :					str        =      []
   A_       :					Optional[Any]        =      []
   _open.append(snake_case__						)

   while _open:
      A_       :					int        =      np.argmin([n.f for n in _open]						)
      A_       :					List[Any]        =      _open[min_f]
      _closed.append(_open.pop(snake_case__						)						)
      if current == goal:
         break
      for n in world.get_neigbours(snake_case__						):
         for c in _closed:
            if c == n:
               continue
         A_       :					Tuple        =      current.g + 1
         A_   ,    A_       :					Optional[Any]        =      n.position
         A_   ,    A_       :					List[str]        =      goal.position
         A_       :					int        =      (ya - ya) ** 2 + (xa - xa) ** 2
         A_       :					Any        =      n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(snake_case__						)
   A_       :					List[Any]        =      []
   while current.parent is not None:
      path.append(current.position						)
      A_       :					str        =      current.parent
   path.append(current.position						)
   return path[::-1]


if __name__ == "__main__":
 _lowerCAmelCase				:   Optional[Any]						       =						Gridworld()
 # Start position and goal
 _lowerCAmelCase				:   str						       =						Cell()
 _lowerCAmelCase				:   Tuple						       =						(0, 0)
 _lowerCAmelCase				:   int						       =						Cell()
 _lowerCAmelCase				:   str						       =						(4, 4)
 print(F'''path from {start.position} to {goal.position}''')
 _lowerCAmelCase				:   Any						       =						astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  _lowerCAmelCase				:   int						       =						1
 print(world.w)


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 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   filter(lambda snake_case__   : p.requires_grad		,  model.parameters()   )
				lowercase_	    =   sum([np.prod(p.size()   ) for p in model_parameters]   )
				return params


__a         =   logging.getLogger(__name__)




def 							a		(				snake_case__:   int		,  snake_case__:   Any   ):


				'''simple docstring'''
				if metric == "rouge2":
								lowercase_	    =   '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								lowercase_	    =   '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								lowercase_	    =   '''{val_avg_em:.4f}-{step_count}'''
				elif metric == "loss":
								lowercase_	    =   '''{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.'''   )

				lowercase_	    =   ModelCheckpoint(
				    dirpath=snake_case__		,  filename=snake_case__		,  monitor=F'''val_{metric}'''		,  mode='''max'''		,  save_top_k=1		,  every_n_epochs=1		,  )

				return checkpoint_callback




def 							a		(				snake_case__:   str		,  snake_case__:   List[str]   ):


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


class 					lowercase__(							pl.Callback				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			)			->	None:
					logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
					lowercase_	    =   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
					lowercase_	    =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
									lowercase_	    =   od / '''test_results.txt'''
									lowercase_	    =   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.
									lowercase_	    =   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
									lowercase_	    =   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
									results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
									generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
					with open(SCREAMING_SNAKE_CASE_			,				'''a+'''			) as writer:
									for key in sorted(SCREAMING_SNAKE_CASE_			):
													if key in ["log", "progress_bar", "preds"]:
																	continue
													lowercase_	    =   metrics[key]
													if isinstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			):
																	lowercase_	    =   val.item()
													lowercase_	    =   f'''{key}: {val:.6f}\n'''
													writer.write(SCREAMING_SNAKE_CASE_			)

					if not save_generations:
									return

					if "preds" in metrics:
									lowercase_	    =   '''\n'''.join(metrics['''preds''']			)
									generations_file.open('''w+'''			).write(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					try:
									lowercase_	    =   pl_module.model.model.num_parameters()
					except AttributeError:
									lowercase_	    =   pl_module.model.num_parameters()

					lowercase_	    =   count_trainable_parameters(SCREAMING_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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			)			->	Any:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					return self._write_logs(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				'''test'''			)




	@rank_zero_only
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Dict:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")




