





import pickle

import numpy as np
from matplotlib import pyplot as plt





class 				A_				:







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[int]  )						->					Optional[int]:
         # save model dict with pickle
         _lowercase							= {
             '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    __UpperCAmelCase (    cls	:	Optional[int]						,__A	:	Tuple  )						->					int:
         # read saved model
         with open(__A						,'rb'  ) as f:
           _lowercase							= pickle.load(__A  )  # noqa: S301

         _lowercase							= model_dic.get('conv1'  )
         conv_get.append(model_dic.get('step_conv1'  )  )
         _lowercase							= model_dic.get('size_pooling1'  )
         _lowercase							= model_dic.get('num_bp1'  )
         _lowercase							= model_dic.get('num_bp2'  )
         _lowercase							= model_dic.get('num_bp3'  )
         _lowercase							= model_dic.get('rate_weight'  )
         _lowercase							= model_dic.get('rate_thre'  )
         # create model instance
         _lowercase							= CNN(__A						,__A						,__A						,__A						,__A						,__A						,__A  )
         # modify model parameter
         _lowercase							= model_dic.get('w_conv1'  )
         _lowercase							= model_dic.get('wkj'  )
         _lowercase							= model_dic.get('vji'  )
         _lowercase							= model_dic.get('thre_conv1'  )
         _lowercase							= model_dic.get('thre_bp2'  )
         _lowercase							= model_dic.get('thre_bp3'  )
         return conv_ins







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	List[Any]  )						->					Tuple:
         return 1 / (1 + np.exp(-1 * x  ))







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[Any]  )						->					str:
         return round(__A						,3  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Optional[Any]						,__A	:	str						,__A	:	Union[str, Any]						,__A	:	Union[str, Any]						,__A	:	int  )						->					List[Any]:
         # convolution process
         _lowercase							= convs[0]
         _lowercase							= convs[1]
         _lowercase							= np.shape(__A  )[0]
         # get the data slice of original image data, data_focus
         _lowercase							= []
         for i_focus in range(0						,size_data - size_conv + 1						,__A  ):
           for j_focus in range(0						,size_data - size_conv + 1						,__A  ):
             _lowercase							= 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
         _lowercase							= []
         _lowercase							= int((size_data - size_conv) / conv_step + 1  )
         for i_map in range(__A  ):
           _lowercase							= []
           for i_focus in range(len(__A  )  ):
             _lowercase							= (
                 np.sum(np.multiply(data_focus[i_focus]						,w_convs[i_map]  )  )
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(__A  )  )
           _lowercase							= np.asmatrix(__A  ).reshape(
               __A						,__A  )
           data_featuremap.append(__A  )

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







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, Any]						,__A	:	Optional[int]						,__A	:	Optional[Any]="average_pool"  )						->					Dict:
         # pooling process
         _lowercase							= len(featuremaps[0]  )
         _lowercase							= int(size_map / size_pooling  )
         _lowercase							= []
         for i_map in range(len(__A  )  ):
           _lowercase							= featuremaps[i_map]
           _lowercase							= []
           for i_focus in range(0						,__A						,__A  ):
             for j_focus in range(0						,__A						,__A  ):
               _lowercase							= 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  )  )
           _lowercase							= np.asmatrix(__A  ).reshape(__A						,__A  )
           featuremap_pooled.append(__A  )
         return featuremap_pooled







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Union[str, Any]  )						->					Optional[int]:
         # expanding three dimension data to one dimension list
         _lowercase							= []
         for i in range(len(__A  )  ):
           _lowercase							= np.shape(data[i]  )
           _lowercase							= data[i].reshape(1						,shapes[0] * shapes[1]  )
           _lowercase							= data_listed.getA().tolist()[0]
           data_expanded.extend(__A  )
         _lowercase							= np.asarray(__A  )
         return data_expanded







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	Dict  )						->					Dict:
         # expanding matrix to one dimension list
         _lowercase							= np.asarray(__A  )
         _lowercase							= np.shape(__A  )
         _lowercase							= data_mat.reshape(1						,shapes[0] * shapes[1]  )
         return data_expanded







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







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

             _lowercase							= np.dot(__A						,self.vji.T  ) - self.thre_bpa
             _lowercase							= self.sig(__A  )
             _lowercase							= np.dot(__A						,self.wkj.T  ) - self.thre_bpa
             _lowercase							= self.sig(__A  )

             # --------------Model Leaning ------------------------
             # calculate error and gradient---------------
             _lowercase							= np.multiply(
                 (data_teach - bp_outa)						,np.multiply(__A						,(1 - bp_outa)  )  )
             _lowercase							= np.multiply(
                 np.dot(__A						,self.wkj  )						,np.multiply(__A						,(1 - bp_outa)  )  )
             _lowercase							= np.dot(__A						,self.vji  )

             _lowercase							= pd_i_all / (self.size_poolinga * self.size_poolinga)
             _lowercase							= pd_conva_pooled.T.getA().tolist()
             _lowercase							= 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]  ):
               _lowercase							= self._expand_mat(pd_conva_all[k_conv]  )
               _lowercase							= self.rate_weight * np.dot(__A						,__A  )

               _lowercase							= self.w_conva[k_conv] + delta_w.reshape(
                   (self.conva[0], self.conva[0])  )

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

         def draw_error():
           _lowercase							= [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    __UpperCAmelCase (    self	:	Tuple						,__A	:	Dict  )						->					List[Any]:
         # model predict
         _lowercase							= []
         print('-------------------Start Testing-------------------------'  )
         print((' - - Shape: Test_Data  ', np.shape(__A  ))  )
         for p in range(len(__A  )  ):
           _lowercase							= np.asmatrix(datas_test[p]  )
           _lowercase						,		_lowercase							= self.convolute(
               __A						,self.conva						,self.w_conva						,self.thre_conva						,conv_step=self.step_conva						,)
           _lowercase							= self.pooling(__A						,self.size_poolinga  )
           _lowercase							= self._expand(__A  )

           _lowercase							= data_bp_input
           _lowercase							= bp_outa * self.vji.T - self.thre_bpa
           _lowercase							= self.sig(__A  )
           _lowercase							= bp_outa * self.wkj.T - self.thre_bpa
           _lowercase							= self.sig(__A  )
           produce_out.extend(bp_outa.getA().tolist()  )
         _lowercase							= [list(map(self.do_round						,__A  )  ) for each in produce_out]
         return np.asarray(__A  )







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Tuple:
         # return the data of image after convoluting process so we can check it out
         _lowercase							= np.asmatrix(__A  )
         _lowercase						,		_lowercase							= self.convolute(
             __A						,self.conva						,self.w_conva						,self.thre_conva						,conv_step=self.step_conva						,)
         _lowercase							= self.pooling(__A						,self.size_poolinga  )

         return data_conveda, data_pooleda


if __name__ == "__main__":
      pass






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :int     )      ->						list[str]:
  return [sentence[i : i + ngram_size] for i in range(len(snake_case__     ) - ngram_size + 1     )]


if __name__ == "__main__":
      from doctest import testmod

      testmod()