
'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=0.2							,		lowerCAmelCase_=0.2 ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       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(A__							,		"""wb""" ) as f:
         pickle.dump(A__							,		A__ )

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


     @classmethod
     def 							_lowerCAmelCase  (      cls							,		lowerCAmelCase_ ):

       '''simple docstring'''

       with open(A__							,		"""rb""" ) as f:
         a_      :	Tuple      =							pickle.load(A__ )  # noqa: S301

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return 1 / (1 + np.exp(-1 * x ))


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return round(A__							,		3 )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

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

       # expanding the data slice to One dimenssion
       a_      :	List[str]      =							[]
       for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(A__ ) )
       a_      :	Optional[int]      =							np.asarray(A__ )
       return focus_list, data_featuremap


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_="average_pool" ):

       '''simple docstring'''

       a_      :	Tuple      =							len(featuremaps[0] )
       a_      :	Optional[Any]      =							int(size_map / size_pooling )
       a_      :	Dict      =							[]
       for i_map in range(len(A__ ) ):
         a_      :	List[Any]      =							featuremaps[i_map]
         a_      :	Union[str, Any]      =							[]
         for i_focus in range(0							,		A__							,		A__ ):
           for j_focus in range(0							,		A__							,		A__ ):
             a_      :	Optional[int]      =							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(A__ ) )
             elif pooling_type == "max_pooling":
               # max pooling
               map_pooled.append(np.max(A__ ) )
         a_      :	Tuple      =							np.asmatrix(A__ ).reshape(A__							,		A__ )
         featuremap_pooled.append(A__ )
       return featuremap_pooled


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							[]
       for i in range(len(A__ ) ):
         a_      :	List[str]      =							np.shape(data[i] )
         a_      :	Dict      =							data[i].reshape(1							,		shapes[0] * shapes[1] )
         a_      :	List[str]      =							data_listed.getA().tolist()[0]
         data_expanded.extend(A__ )
       a_      :	Optional[Any]      =							np.asarray(A__ )
       return data_expanded


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							np.asarray(A__ )
       a_      :	int      =							np.shape(A__ )
       a_      :	Optional[int]      =							data_mat.reshape(1							,		shapes[0] * shapes[1] )
       return data_expanded


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	str      =							[]
       a_      :	int      =							0
       for i_map in range(A__ ):
         a_      :	Any      =							np.ones((size_map, size_map) )
         for i in range(0							,		A__							,		A__ ):
           for j in range(0							,		A__							,		A__ ):
             a_      :	Union[str, Any]      =							pd_pool[
                 i_pool
             ]
             a_      :	Optional[int]      =							i_pool + 1
         a_      :	Any      =							np.multiply(
             A__							,		np.multiply(out_map[i_map]							,		(1 - out_map[i_map]) ) )
         pd_all.append(A__ )
       return pd_all


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=bool ):

       '''simple docstring'''

       print("""----------------------Start Training-------------------------""" )
       print((""" - - Shape: Train_Data  """, np.shape(A__ )) )
       print((""" - - Shape: Teach_Data  """, np.shape(A__ )) )
       a_      :	Optional[Any]      =							0
       a_      :	Any      =							[]
       a_      :	Tuple      =							1_00_00
       while rp < n_repeat and mse >= error_accuracy:
         a_      :	Optional[int]      =							0
         print(f'''-------------Learning Time {rp}--------------''' )
         for p in range(len(A__ ) ):
           # print('------------Learning Image: %d--------------'%p)
           a_      :	Dict      =							np.asmatrix(datas_train[p] )
           a_      :	Dict      =							np.asarray(datas_teach[p] )
           a_      , a_      :	Union[str, Any]      =							self.convolute(
               A__							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
           a_      :	Any      =							self.pooling(A__							,		self.size_poolinga )
           a_      :	Dict      =							np.shape(A__ )
           a_      :	Tuple      =							self._expand(A__ )
           a_      :	Tuple      =							data_bp_input

           a_      :	Tuple      =							np.dot(A__							,		self.vji.T ) - self.thre_bpa
           a_      :	Any      =							self.sig(A__ )
           a_      :	Dict      =							np.dot(A__							,		self.wkj.T ) - self.thre_bpa
           a_      :	List[Any]      =							self.sig(A__ )

           # --------------Model Leaning ------------------------
           # calculate error and gradient---------------
           a_      :	List[Any]      =							np.multiply(
               (data_teach - bp_outa)							,		np.multiply(A__							,		(1 - bp_outa) ) )
           a_      :	List[Any]      =							np.multiply(
               np.dot(A__							,		self.wkj )							,		np.multiply(A__							,		(1 - bp_outa) ) )
           a_      :	Tuple      =							np.dot(A__							,		self.vji )

           a_      :	Optional[int]      =							pd_i_all / (self.size_poolinga * self.size_poolinga)
           a_      :	Any      =							pd_conva_pooled.T.getA().tolist()
           a_      :	str      =							self._calculate_gradient_from_pool(
               A__							,		A__							,		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_      :	Dict      =							self._expand_mat(pd_conva_all[k_conv] )
             a_      :	str      =							self.rate_weight * np.dot(A__							,		A__ )

             a_      :	List[str]      =							self.w_conva[k_conv] + delta_w.reshape(
                 (self.conva[0], self.conva[0]) )

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

       def draw_error():
         a_      :	Union[str, Any]      =							[error_accuracy for i in range(int(n_repeat * 1.2 ) )]
         plt.plot(A__							,		"""+-""" )
         plt.plot(A__							,		"""r--""" )
         plt.xlabel("""Learning Times""" )
         plt.ylabel("""All_mse""" )
         plt.grid(A__							,		alpha=0.5 )
         plt.show()

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							[]
       print("""-------------------Start Testing-------------------------""" )
       print((""" - - Shape: Test_Data  """, np.shape(A__ )) )
       for p in range(len(A__ ) ):
         a_      :	Any      =							np.asmatrix(datas_test[p] )
         a_      , a_      :	Dict      =							self.convolute(
             A__							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
         a_      :	List[str]      =							self.pooling(A__							,		self.size_poolinga )
         a_      :	str      =							self._expand(A__ )

         a_      :	Union[str, Any]      =							data_bp_input
         a_      :	List[str]      =							bp_outa * self.vji.T - self.thre_bpa
         a_      :	str      =							self.sig(A__ )
         a_      :	Tuple      =							bp_outa * self.wkj.T - self.thre_bpa
         a_      :	Any      =							self.sig(A__ )
         produce_out.extend(bp_outa.getA().tolist() )
       a_      :	Union[str, Any]      =							[list(map(self.do_round							,		A__ ) ) for each in produce_out]
       return np.asarray(A__ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							np.asmatrix(A__ )
       a_      , a_      :	Optional[int]      =							self.convolute(
           A__							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
       a_      :	Tuple      =							self.pooling(A__							,		self.size_poolinga )

       return data_conveda, data_pooleda


if __name__ == "__main__":
       pass




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


