


import pickle

import numpy as np
from matplotlib import pyplot as plt

class 							lowercase  :







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







     def 							lowercase__       (						self :						int   ,   _lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		{
               '''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(__UpperCAmelCase   ,   '''wb'''      ) as f:
                 pickle.dump(__UpperCAmelCase   ,   __UpperCAmelCase      )

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







     @classmethod
     def 							lowercase__       (						cls :						Optional[int]   ,   _lowercase :						Tuple      ):


           with open(__UpperCAmelCase   ,   '''rb'''      ) as f:
                 SCREAMING_SNAKE_CASE__       :  str						     =		pickle.load(__UpperCAmelCase      )  # noqa: S301

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







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Optional[int]      ):
           return 1 / (1 + np.exp(-1 * x      ))







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						List[str]      ):
           return round(__UpperCAmelCase   ,   3      )







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

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







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







     def 							lowercase__       (						self :						Union[str, Any]   ,   _lowercase :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[]
           for i in range(len(__UpperCAmelCase      )      ):
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		np.shape(data[i]      )
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		data[i].reshape(1   ,   shapes[0] * shapes[1]      )
                 SCREAMING_SNAKE_CASE__       :  Any						     =		data_listed.getA().tolist()[0]
                 data_expanded.extend(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		np.asarray(__UpperCAmelCase      )
           return data_expanded







     def 							lowercase__       (						self :						Any   ,   _lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		np.asarray(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		np.shape(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		data_mat.reshape(1   ,   shapes[0] * shapes[1]      )
           return data_expanded







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







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

                       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		np.dot(__UpperCAmelCase   ,   self.vji.T      ) - self.thre_bpa
                       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.sig(__UpperCAmelCase      )
                       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		np.dot(__UpperCAmelCase   ,   self.wkj.T      ) - self.thre_bpa
                       SCREAMING_SNAKE_CASE__       :  Dict						     =		self.sig(__UpperCAmelCase      )

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

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

                             SCREAMING_SNAKE_CASE__       :  Dict						     =		self.w_conva[k_conv] + delta_w.reshape(
                                 (self.conva[0], self.conva[0])      )

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

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

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







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

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







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		np.asmatrix(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.convolute(
               __UpperCAmelCase   ,   self.conva   ,   self.w_conva   ,   self.thre_conva   ,   conv_step=self.step_conva   ,   )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.pooling(__UpperCAmelCase   ,   self.size_poolinga      )

           return data_conveda, data_pooleda


if __name__ == "__main__":
    pass














from ... import PretrainedConfig


UpperCAmelCase__							  =						{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
      __snake_case	    =    '''nezha'''







      def __init__( self :		Union[str, Any]					,   __UpperCAmelCase :		List[str]=21_128					,   __UpperCAmelCase :		Any=768					,   __UpperCAmelCase :		int=12					,   __UpperCAmelCase :		Optional[int]=12					,   __UpperCAmelCase :		Tuple=3_072					,   __UpperCAmelCase :		Union[str, Any]="gelu"					,   __UpperCAmelCase :		List[str]=0.1					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		str=512					,   __UpperCAmelCase :		List[Any]=64					,   __UpperCAmelCase :		Tuple=2					,   __UpperCAmelCase :		List[Any]=0.02					,   __UpperCAmelCase :		List[str]=1e-1_2					,   __UpperCAmelCase :		int=0.1					,   __UpperCAmelCase :		Dict=0					,   __UpperCAmelCase :		Any=2					,   __UpperCAmelCase :		Optional[Any]=3					,   __UpperCAmelCase :		int=True					,   **__UpperCAmelCase :		Optional[Any]					,   )       ->Any:




       """simple docstring"""


       super().__init__(pad_token_id=__UpperCAmelCase					,   bos_token_id=__UpperCAmelCase					,   eos_token_id=__UpperCAmelCase					,   **__UpperCAmelCase    )

       a						    = vocab_size
       a						    = hidden_size
       a						    = num_hidden_layers
       a						    = num_attention_heads
       a						    = hidden_act
       a						    = intermediate_size
       a						    = hidden_dropout_prob
       a						    = attention_probs_dropout_prob
       a						    = max_position_embeddings
       a						    = max_relative_position
       a						    = type_vocab_size
       a						    = initializer_range
       a						    = layer_norm_eps
       a						    = classifier_dropout
       a						    = use_cache


