


import pickle

import numpy as np
from matplotlib import pyplot as plt







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase=0.2					,  _lowerCamelCase=0.2    ):
       a						:Tuple 							=       bp_numa
       a						:Tuple 							=       bp_numa
       a						:int 							=       bp_numa
       a						:Optional[Any] 							=       conva_get[:2]
       a						:Dict 							=       conva_get[2]
       a						:str 							=       size_pa
       a						:str 							=       rate_w
       a						:List[str] 							=       rate_t
       a						:Union[str, Any] 							=       [
           np.mat(-1 * np.random.rand(self.conva[0]					,  self.conva[0]    ) + 0.5    )
           for i in range(self.conva[1]    )
       ]
       a						:Any 							=       np.mat(-1 * np.random.rand(self.num_bpa					,  self.num_bpa    ) + 0.5    )
       a						:Union[str, Any] 							=       np.mat(-1 * np.random.rand(self.num_bpa					,  self.num_bpa    ) + 0.5    )
       a						:Tuple 							=       -2 * np.random.rand(self.conva[1]    ) + 1
       a						:Any 							=       -2 * np.random.rand(self.num_bpa    ) + 1
       a						:Optional[int] 							=       -2 * np.random.rand(self.num_bpa    ) + 1




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # save model dict with pickle
       a						:Any 							=       {
           '''num_bp1''': self.num_bpa,
           '''num_bp2''': self.num_bpa,
           '''num_bp3''': self.num_bpa,
           '''conv1''': self.conva,
           '''step_conv1''': self.step_conva,
           '''size_pooling1''': self.size_poolinga,
           '''rate_weight''': self.rate_weight,
           '''rate_thre''': self.rate_thre,
           '''w_conv1''': self.w_conva,
           '''wkj''': self.wkj,
           '''vji''': self.vji,
           '''thre_conv1''': self.thre_conva,
           '''thre_bp2''': self.thre_bpa,
           '''thre_bp3''': self.thre_bpa,
       }
       with open(_lowerCamelCase					,  '''wb'''    ) as f:
             pickle.dump(_lowerCamelCase					,  _lowerCamelCase    )

       print(F'''Model saved： {save_path}'''    )




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls					,  _lowerCamelCase    ):
       # read saved model
       with open(_lowerCamelCase					,  '''rb'''    ) as f:
             a						:str 							=       pickle.load(_lowerCamelCase    )  # noqa: S301

       a						:List[str] 							=       model_dic.get('''conv1'''    )
       conv_get.append(model_dic.get('''step_conv1'''    )    )
       a						:Optional[int] 							=       model_dic.get('''size_pooling1'''    )
       a						:str 							=       model_dic.get('''num_bp1'''    )
       a						:List[Any] 							=       model_dic.get('''num_bp2'''    )
       a						:Dict 							=       model_dic.get('''num_bp3'''    )
       a						:str 							=       model_dic.get('''rate_weight'''    )
       a						:str 							=       model_dic.get('''rate_thre'''    )
       # create model instance
       a						:Optional[Any] 							=       CNN(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       # modify model parameter
       a						:Optional[Any] 							=       model_dic.get('''w_conv1'''    )
       a						:List[str] 							=       model_dic.get('''wkj'''    )
       a						:List[Any] 							=       model_dic.get('''vji'''    )
       a						:Optional[int] 							=       model_dic.get('''thre_conv1'''    )
       a						:Any 							=       model_dic.get('''thre_bp2'''    )
       a						:Optional[int] 							=       model_dic.get('''thre_bp3'''    )
       return conv_ins




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return 1 / (1 + np.exp(-1 * x    ))




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return round(_lowerCamelCase					,  3    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       # convolution process
       a						:Dict 							=       convs[0]
       a						:Optional[Any] 							=       convs[1]
       a						:Union[str, Any] 							=       np.shape(_lowerCamelCase    )[0]
       # get the data slice of original image data, data_focus
       a						:List[Any] 							=       []
       for i_focus in range(0					,  size_data - size_conv + 1					,  _lowerCamelCase    ):
             for j_focus in range(0					,  size_data - size_conv + 1					,  _lowerCamelCase    ):
                   a						:Union[str, Any] 							=       data[
                       i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                   ]
                   data_focus.append(_lowerCamelCase    )
        # calculate the feature map of every single kernel, and saved as list of matrix
       a						:int 							=       []
       a						:List[Any] 							=       int((size_data - size_conv) / conv_step + 1    )
       for i_map in range(_lowerCamelCase    ):
             a						:Tuple 							=       []
             for i_focus in range(len(_lowerCamelCase    )    ):
                   a						:str 							=       (
                       np.sum(np.multiply(data_focus[i_focus]					,  w_convs[i_map]    )    )
                       - thre_convs[i_map]
                   )
                   featuremap.append(self.sig(_lowerCamelCase    )    )
             a						:str 							=       np.asmatrix(_lowerCamelCase    ).reshape(
                 _lowerCamelCase					,  _lowerCamelCase    )
             data_featuremap.append(_lowerCamelCase    )

       # expanding the data slice to One dimenssion
       a						:Any 							=       []
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(_lowerCamelCase    )    )
       a						:Any 							=       np.asarray(_lowerCamelCase    )
       return focus_list, data_featuremap




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase="average_pool"    ):
       # pooling process
       a						:Any 							=       len(featuremaps[0]    )
       a						:List[str] 							=       int(size_map / size_pooling    )
       a						:List[str] 							=       []
       for i_map in range(len(_lowerCamelCase    )    ):
             a						:Optional[int] 							=       featuremaps[i_map]
             a						:str 							=       []
             for i_focus in range(0					,  _lowerCamelCase					,  _lowerCamelCase    ):
                   for j_focus in range(0					,  _lowerCamelCase					,  _lowerCamelCase    ):
                         a						:Union[str, Any] 							=       feature_map[
                             i_focus : i_focus + size_pooling,
                             j_focus : j_focus + size_pooling,
                         ]
                         if pooling_type == "average_pool":
                               # average pooling
                               map_pooled.append(np.average(_lowerCamelCase    )    )
                         elif pooling_type == "max_pooling":
                               # max pooling
                               map_pooled.append(np.max(_lowerCamelCase    )    )
             a						:Dict 							=       np.asmatrix(_lowerCamelCase    ).reshape(_lowerCamelCase					,  _lowerCamelCase    )
             featuremap_pooled.append(_lowerCamelCase    )
       return featuremap_pooled




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # expanding three dimension data to one dimension list
       a						:Optional[Any] 							=       []
       for i in range(len(_lowerCamelCase    )    ):
             a						:Optional[int] 							=       np.shape(data[i]    )
             a						:Tuple 							=       data[i].reshape(1					,  shapes[0] * shapes[1]    )
             a						:List[Any] 							=       data_listed.getA().tolist()[0]
             data_expanded.extend(_lowerCamelCase    )
       a						:List[Any] 							=       np.asarray(_lowerCamelCase    )
       return data_expanded




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # expanding matrix to one dimension list
       a						:Optional[Any] 							=       np.asarray(_lowerCamelCase    )
       a						:Any 							=       np.shape(_lowerCamelCase    )
       a						:Optional[Any] 							=       data_mat.reshape(1					,  shapes[0] * shapes[1]    )
       return data_expanded




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       []
       a						:List[str] 							=       0
       for i_map in range(_lowerCamelCase    ):
             a						:List[str] 							=       np.ones((size_map, size_map)    )
             for i in range(0					,  _lowerCamelCase					,  _lowerCamelCase    ):
                   for j in range(0					,  _lowerCamelCase					,  _lowerCamelCase    ):
                         a						:Optional[int] 							=       pd_pool[
                             i_pool
                         ]
                         a						:int 							=       i_pool + 1
             a						:Optional[Any] 							=       np.multiply(
                 _lowerCamelCase					,  np.multiply(out_map[i_map]					,  (1 - out_map[i_map])    )    )
             pd_all.append(_lowerCamelCase    )
       return pd_all




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase=bool    ):
       # model traning
       print('''----------------------Start Training-------------------------'''    )
       print((''' - - Shape: Train_Data  ''', np.shape(_lowerCamelCase    ))    )
       print((''' - - Shape: Teach_Data  ''', np.shape(_lowerCamelCase    ))    )
       a						:Union[str, Any] 							=       0
       a						:List[Any] 							=       []
       a						:Optional[Any] 							=       1_0000
       while rp < n_repeat and mse >= error_accuracy:
             a						:Optional[Any] 							=       0
             print(F'''-------------Learning Time {rp}--------------'''    )
             for p in range(len(_lowerCamelCase    )    ):
                   # print('------------Learning Image: %d--------------'%p)
                   a						:List[Any] 							=       np.asmatrix(datas_train[p]    )
                   a						:int 							=       np.asarray(datas_teach[p]    )
                   a							,				a						:Union[str, Any] 							=       self.convolute(
                       _lowerCamelCase					,  self.conva					,  self.w_conva					,  self.thre_conva					,  conv_step=self.step_conva					,  )
                   a						:Optional[int] 							=       self.pooling(_lowerCamelCase					,  self.size_poolinga    )
                   a						:Optional[int] 							=       np.shape(_lowerCamelCase    )
                   a						:List[str] 							=       self._expand(_lowerCamelCase    )
                   a						:Tuple 							=       data_bp_input

                   a						:str 							=       np.dot(_lowerCamelCase					,  self.vji.T    ) - self.thre_bpa
                   a						:Optional[Any] 							=       self.sig(_lowerCamelCase    )
                   a						:str 							=       np.dot(_lowerCamelCase					,  self.wkj.T    ) - self.thre_bpa
                   a						:Union[str, Any] 							=       self.sig(_lowerCamelCase    )

                   # --------------Model Leaning ------------------------
                   # calculate error and gradient---------------
                   a						:Any 							=       np.multiply(
                       (data_teach - bp_outa)					,  np.multiply(_lowerCamelCase					,  (1 - bp_outa)    )    )
                   a						:str 							=       np.multiply(
                       np.dot(_lowerCamelCase					,  self.wkj    )					,  np.multiply(_lowerCamelCase					,  (1 - bp_outa)    )    )
                   a						:int 							=       np.dot(_lowerCamelCase					,  self.vji    )

                   a						:Any 							=       pd_i_all / (self.size_poolinga * self.size_poolinga)
                   a						:List[str] 							=       pd_conva_pooled.T.getA().tolist()
                   a						:Optional[int] 							=       self._calculate_gradient_from_pool(
                       _lowerCamelCase					,  _lowerCamelCase					,  shape_featuremapa[0]					,  shape_featuremapa[1]					,  self.size_poolinga					,  )
                   # weight and threshold learning process---------
                   # convolution layer
                   for k_conv in range(self.conva[1]    ):
                         a						:Optional[int] 							=       self._expand_mat(pd_conva_all[k_conv]    )
                         a						:List[Any] 							=       self.rate_weight * np.dot(_lowerCamelCase					,  _lowerCamelCase    )

                         a						:Union[str, Any] 							=       self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])    )

                         a						:Any 							=       (
                             self.thre_conva[k_conv]
                             - np.sum(pd_conva_all[k_conv]    ) * self.rate_thre
                         )
                   # all connected layer
                   a						:str 							=       self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                   a						:List[Any] 							=       self.vji + pd_j_all.T * bp_outa * self.rate_weight
                   a						:Dict 							=       self.thre_bpa - pd_k_all * self.rate_thre
                   a						:Optional[Any] 							=       self.thre_bpa - pd_j_all * self.rate_thre
                   # calculate the sum error of all single image
                   a						:Union[str, Any] 							=       np.sum(abs(data_teach - bp_outa    )    )
                   error_count += errors
                   # print('   ----Teach      ',data_teach)
                   # print('   ----BP_output  ',bp_out3)
             a						:List[str] 							=       rp + 1
             a						:List[str] 							=       error_count / patterns
             all_mse.append(_lowerCamelCase    )

       def draw_error():
             a						:Optional[Any] 							=       [error_accuracy for i in range(int(n_repeat * 1.2    )    )]
             plt.plot(_lowerCamelCase					,  '''+-'''    )
             plt.plot(_lowerCamelCase					,  '''r--'''    )
             plt.xlabel('''Learning Times'''    )
             plt.ylabel('''All_mse'''    )
             plt.grid(_lowerCamelCase					,  alpha=0.5    )
             plt.show()

       print('''------------------Training Complished---------------------'''    )
       print((''' - - Training epoch: ''', rp, F'''     - - Mse: {mse:.6f}''')    )
       if draw_e:
             draw_error()
       return mse




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # model predict
       a						:Any 							=       []
       print('''-------------------Start Testing-------------------------'''    )
       print((''' - - Shape: Test_Data  ''', np.shape(_lowerCamelCase    ))    )
       for p in range(len(_lowerCamelCase    )    ):
             a						:Dict 							=       np.asmatrix(datas_test[p]    )
             a							,				a						:Union[str, Any] 							=       self.convolute(
                 _lowerCamelCase					,  self.conva					,  self.w_conva					,  self.thre_conva					,  conv_step=self.step_conva					,  )
             a						:Optional[int] 							=       self.pooling(_lowerCamelCase					,  self.size_poolinga    )
             a						:int 							=       self._expand(_lowerCamelCase    )

             a						:Optional[int] 							=       data_bp_input
             a						:Dict 							=       bp_outa * self.vji.T - self.thre_bpa
             a						:List[Any] 							=       self.sig(_lowerCamelCase    )
             a						:Optional[int] 							=       bp_outa * self.wkj.T - self.thre_bpa
             a						:Tuple 							=       self.sig(_lowerCamelCase    )
             produce_out.extend(bp_outa.getA().tolist()    )
       a						:Optional[Any] 							=       [list(map(self.do_round					,  _lowerCamelCase    )    ) for each in produce_out]
       return np.asarray(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # return the data of image after convoluting process so we can check it out
       a						:Union[str, Any] 							=       np.asmatrix(_lowerCamelCase    )
       a							,				a						:str 							=       self.convolute(
           _lowerCamelCase					,  self.conva					,  self.w_conva					,  self.thre_conva					,  conv_step=self.step_conva					,  )
       a						:str 							=       self.pooling(_lowerCamelCase					,  self.size_poolinga    )

       return data_conveda, data_pooleda


if __name__ == "__main__":
     pass












import heapq
import sys

import numpy as np

snake_case__					:							Tuple													=     tuple[int, int]





class A_					:




   def __init__(self	:Union[str, Any] )->					Union[str, Any]:
         __A          =				[]
         __A          =				set()




   def _lowerCAmelCase      (self	:List[str] )->					str:
         if not self.empty():
               return self.elements[0][0]
         else:
               return float('''inf''' )




   def _lowerCAmelCase      (self	:Any )->					Any:
         return len(self.elements ) == 0




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int] )->					Optional[int]:
         if item not in self.set:
               heapq.heappush(self.elements    ,   (priority, item) )
               self.set.add(_UpperCamelCase )
         else:
               # update
               # print("update", item)
               __A          =				[]
               ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               while x != item:
                     temp.append((pri, x) )
                     ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               temp.append((priority, item) )
               for pro, xxx in temp:
                     heapq.heappush(self.elements    ,   (pro, xxx) )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:int )->					int:
         if item in self.set:
               self.set.remove(_UpperCamelCase )
               __A          =				[]
               ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               while x != item:
                     temp.append((pro, x) )
                     ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               for prito, yyy in temp:
                     heapq.heappush(self.elements    ,   (prito, yyy) )




   def _lowerCAmelCase      (self	:Optional[Any] )->					int:
         return self.elements[0][1]






   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
         self.set.remove(_UpperCamelCase )
         return (priority, item)





def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      __A          =				np.array(lowerCamelCase )
      return np.linalg.norm(a - b )



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						List[str]:







      '''simple docstring'''






      return consistent_heuristic(lowerCamelCase					,				lowerCamelCase ) // t



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






      return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   int					,				lowerCamelCase:   TPos					,				lowerCamelCase:   dict[TPos, float] )	->						Tuple:







      '''simple docstring'''






      __A          =				g_function[start] + Wa * heuristics[i](lowerCamelCase					,				lowerCamelCase )
      return ans



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				np.chararray((n, n) )
      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  __A          =				'''*'''

      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  if (j, (n - 1) - i) in blocks:
                        __A          =				'''#'''

      __A          =				'''-'''
      __A          =				back_pointer[goal]
      while x != start:
            ((__A)   ,						(__A))          =				x
            # print(x)
            __A          =				'''-'''
            __A          =				back_pointer[x]
      __A          =				'''-'''

      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]					,				end=''' ''' )
                        print('''<-- End position'''					,				end=''' ''' )
                  else:
                        print(grid[i][j]					,				end=''' ''' )
            print()
      print('''^''' )
      print('''Start position''' )
      print()
      print('''# is an obstacle''' )
      print('''- is the path taken by algorithm''' )
      print('''PATH TAKEN BY THE ALGORITHM IS:-''' )
      __A          =				back_pointer[goal]
      while x != start:
            print(lowerCamelCase					,				end=''' ''' )
            __A          =				back_pointer[x]
      print(lowerCamelCase )
      sys.exit()



def _a				(	lowerCamelCase:   TPos )	->						Optional[Any]:







      '''simple docstring'''






      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Dict					,				)	->						Dict:







      '''simple docstring'''






      for itera in range(lowerCamelCase ):
            open_list[itera].remove_element(lowerCamelCase )
      # print("s", s)
      # print("j", j)
      ((__A)   ,						(__A))          =				s
      __A          =				(x - 1, y)
      __A          =				(x + 1, y)
      __A          =				(x, y + 1)
      __A          =				(x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(lowerCamelCase ) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(lowerCamelCase )
                        __A          =				-1
                        __A          =				float('''inf''' )

                  if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
                        __A          =				g_function[s] + 1
                        __A          =				s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(lowerCamelCase					,				key(lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase ) )
                              if neighbours not in close_list_inad:
                                    for var in range(1					,				lowerCamelCase ):
                                          if key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) <= Wa * key(
                                              lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase ):
                                                open_list[j].put(
                                                    lowerCamelCase					,				key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) )



def _a				(	)	->						str:







      '''simple docstring'''






      __A          =				[]
      for x in range(1					,				5 ):
            for y in range(1					,				6 ):
                  some_list.append((x, y) )

      for x in range(15					,				20 ):
            some_list.append((x, 17) )

      for x in range(10					,				19 ):
            for y in range(1					,				15 ):
                  some_list.append((x, y) )

    # L block
      for x in range(1					,				4 ):
            for y in range(12					,				19 ):
                  some_list.append((x, y) )
      for x in range(3					,				13 ):
            for y in range(16					,				19 ):
                  some_list.append((x, y) )
      return some_list


snake_case__					:							Union[str, Any]													=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

snake_case__					:							List[str]													=     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
snake_case__					:							List[str]													=     make_common_ground()


snake_case__					:							int													=     blocks_blk
# hyper parameters
snake_case__					:							Optional[int]													=     1
snake_case__					:							List[Any]													=     1
snake_case__					:							int													=     20
snake_case__					:							Optional[int]													=     3  # one consistent and two other inconsistent

# start and end destination
snake_case__					:							Tuple													=     (0, 0)
snake_case__					:							Any													=     (n - 1, n - 1)

snake_case__					:							List[Any]													=     1



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				{start: 0, goal: float('''inf''' )}
      __A          =				{start: -1, goal: -1}
      __A          =				[]
      __A          =				set()

      for i in range(lowerCamelCase ):
            open_list.append(PriorityQueue() )
            open_list[i].put(lowerCamelCase					,				key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) )

      __A          =				[]
      __A          =				[]
      while open_list[0].minkey() < float('''inf''' ):
            for i in range(1					,				lowerCamelCase ):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float('''inf''' ):
                                    do_something(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        else:
                              __A   ,						__A          =				open_list[i].top_show()
                              visited.add(lowerCamelCase )
                              expand_state(
                                  lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				)
                              close_list_inad.append(lowerCamelCase )
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float('''inf''' ):
                                    do_something(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        else:
                              __A          =				open_list[0].top_show()
                              visited.add(lowerCamelCase )
                              expand_state(
                                  lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				)
                              close_list_anchor.append(lowerCamelCase )
      print('''No path found to goal''' )
      print()
      for i in range(n - 1					,				-1					,				-1 ):
            for j in range(lowerCamelCase ):
                  if (j, i) in blocks:
                        print('''#'''					,				end=''' ''' )
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print('''*'''					,				end=''' ''' )
                        else:
                              print('''-'''					,				end=''' ''' )
                  else:
                        print('''*'''					,				end=''' ''' )
                  if (j, i) == (n - 1, n - 1):
                        print('''<-- End position'''					,				end=''' ''' )
            print()
      print('''^''' )
      print('''Start position''' )
      print()
      print('''# is an obstacle''' )
      print('''- is the path taken by algorithm''' )


if __name__ == "__main__":
       multi_a_star(start, goal, n_heuristic)





