






import numpy as np



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     Any      )      ->							Tuple:
           __UpperCAmelCase     				=(0, 0)
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=0



    def __eq__(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     List[Any]      )      ->							List[Any]:
           return self.position == cell.position



    def _a							(				self      :     str      )      ->							Any:
           print(self.position      )




class 				_A			:
    """simple docstring"""

    def __init__(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple=(5, 5)      )      ->							int:
           __UpperCAmelCase     				=np.zeros(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=world_size[0]
           __UpperCAmelCase     				=world_size[1]



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           print(self.w      )



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							int:
           __UpperCAmelCase     				=[
               (-1, -1),
               (-1, 0),
               (-1, 1),
               (0, -1),
               (0, 1),
               (1, -1),
               (1, 0),
               (1, 1),
           ]
           __UpperCAmelCase     				=cell.position[0]
           __UpperCAmelCase     				=cell.position[1]
           __UpperCAmelCase     				=[]
           for n in neughbour_cord:
                  __UpperCAmelCase     				=current_x + n[0]
                  __UpperCAmelCase     				=current_y + n[1]
                  if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                         __UpperCAmelCase     				=Cell()
                         __UpperCAmelCase     				=(x, y)
                         __UpperCAmelCase     				=cell
                         neighbours.append(__SCREAMING_SNAKE_CASE      )
           return neighbours







def       lowercase__	(					A_:						Dict  ,      A_:						Tuple  ,      A_:						Tuple		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=[]
       __UpperCAmelCase     				=[]
       _open.append(A_		)

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

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


if __name__ == "__main__":
   __A					=	Gridworld()
   # Start position and goal
   __A					=	Cell()
   __A					=	(0, 0)
   __A					=	Cell()
   __A					=	(4, 4)
   print(F"""path from {start.position} to {goal.position}""")
   __A					=	astar(world, start, goal)
   #   Just for visual reasons.
   for i in s:
      __A					=	1
   print(world.w)






"""simple docstring"""

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_            =    '''scheduler_config.json'''
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						1
      __SCREAMING_SNAKE_CASE :				Dict				   =						2
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						3
      __SCREAMING_SNAKE_CASE :				Any				   =						4
      __SCREAMING_SNAKE_CASE :				Any				   =						5
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						6
      __SCREAMING_SNAKE_CASE :				str				   =						7
      __SCREAMING_SNAKE_CASE :				Any				   =						8
      __SCREAMING_SNAKE_CASE :				Tuple				   =						9
      __SCREAMING_SNAKE_CASE :				int				   =						1_0
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_1
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_2
      __SCREAMING_SNAKE_CASE :				Dict				   =						1_3
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						1_4

@dataclass
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						SCHEDULER_CONFIG_NAME
      __SCREAMING_SNAKE_CASE :				Dict				   =						[]
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						True




      @classmethod
      def      a_   (				cls ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=False ,							**lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       cls.load_config(
              pretrained_model_name_or_path=lowercase_ ,							subfolder=lowercase_ ,							return_unused_kwargs=lowercase_ ,							return_commit_hash=lowercase_ ,							**lowercase_ ,							)
          return cls.from_config(lowercase_ ,							return_unused_kwargs=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ = False ,							**lowercase_				)    ->       str:
          self.save_config(save_directory=lowercase_ ,							push_to_hub=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          return self._get_compatibles()




      @classmethod
      def      a_   (				cls				)    ->       Any:
          UpperCAmelCase          =       list(set([cls.__name__] + cls._compatibles				)				)
          UpperCAmelCase          =       importlib.import_module(__name__.split('.'				)[0]				)
          UpperCAmelCase          =       [
              getattr(lowercase_ ,							lowercase_				) for c in compatible_classes_str if hasattr(lowercase_ ,							lowercase_				)
          ]
          return compatible_classes



