


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









snake_case				:    str       				=     '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

snake_case				:    List[Any]       				=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case				:    int       				=     {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





