

'''simple docstring'''
import pickle

import numpy as np
from matplotlib import pyplot as plt







class 							A__   :





  def __init__(		self				:     Tuple							, _a				:     Dict							, _a				:     int							, _a				:     Union[str, Any]							, _a				:     List[Any]							, _a				:     Union[str, Any]							, _a				:     List[Any]=0.2							, _a				:     Tuple=0.2			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =bp_numa
       _SCREAMING_SNAKE_CASE							   =bp_numa
       _SCREAMING_SNAKE_CASE							   =bp_numa
       _SCREAMING_SNAKE_CASE							   =conva_get[:2]
       _SCREAMING_SNAKE_CASE							   =conva_get[2]
       _SCREAMING_SNAKE_CASE							   =size_pa
       _SCREAMING_SNAKE_CASE							   =rate_w
       _SCREAMING_SNAKE_CASE							   =rate_t
       _SCREAMING_SNAKE_CASE							   =[
           np.mat(-1 * np.random.rand(self.conva[0]							, self.conva[0]			) + 0.5			)
           for i in range(self.conva[1]			)
       ]
       _SCREAMING_SNAKE_CASE							   =np.mat(-1 * np.random.rand(self.num_bpa							, self.num_bpa			) + 0.5			)
       _SCREAMING_SNAKE_CASE							   =np.mat(-1 * np.random.rand(self.num_bpa							, self.num_bpa			) + 0.5			)
       _SCREAMING_SNAKE_CASE							   =-2 * np.random.rand(self.conva[1]			) + 1
       _SCREAMING_SNAKE_CASE							   =-2 * np.random.rand(self.num_bpa			) + 1
       _SCREAMING_SNAKE_CASE							   =-2 * np.random.rand(self.num_bpa			) + 1





  def 						A  (		self				:     Optional[Any]							, _a				:     Optional[int]			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   ={
           '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 						A  (		cls				:     Optional[int]							, _a				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






       with open(_a							, 'rb'			) as f:
            _SCREAMING_SNAKE_CASE							   =pickle.load(_a			)  # noqa: S301

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





  def 						A  (		self				:     Optional[int]							, _a				:     Dict			)     ->   Optional[int]:





       '''simple docstring'''






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





  def 						A  (		self				:     Tuple							, _a				:     str			)     ->   Dict:





       '''simple docstring'''






       return round(_a							, 3			)





  def 						A  (		self				:     Any							, _a				:     int							, _a				:     List[Any]							, _a				:     Union[str, Any]							, _a				:     Optional[Any]							, _a				:     List[Any]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =convs[0]
       _SCREAMING_SNAKE_CASE							   =convs[1]
       _SCREAMING_SNAKE_CASE							   =np.shape(_a			)[0]
       # get the data slice of original image data, data_focus
       _SCREAMING_SNAKE_CASE							   =[]
       for i_focus in range(0							, size_data - size_conv + 1							, _a			):
            for j_focus in range(0							, size_data - size_conv + 1							, _a			):
                 _SCREAMING_SNAKE_CASE							   =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
       _SCREAMING_SNAKE_CASE							   =[]
       _SCREAMING_SNAKE_CASE							   =int((size_data - size_conv) / conv_step + 1			)
       for i_map in range(_a			):
            _SCREAMING_SNAKE_CASE							   =[]
            for i_focus in range(len(_a			)			):
                 _SCREAMING_SNAKE_CASE							   =(
                     np.sum(np.multiply(data_focus[i_focus]							, w_convs[i_map]			)			)
                     - thre_convs[i_map]
                 )
                 featuremap.append(self.sig(_a			)			)
            _SCREAMING_SNAKE_CASE							   =np.asmatrix(_a			).reshape(
                _a							, _a			)
            data_featuremap.append(_a			)

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





  def 						A  (		self				:     int							, _a				:     Dict							, _a				:     Tuple							, _a				:     List[Any]="average_pool"			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =len(featuremaps[0]			)
       _SCREAMING_SNAKE_CASE							   =int(size_map / size_pooling			)
       _SCREAMING_SNAKE_CASE							   =[]
       for i_map in range(len(_a			)			):
            _SCREAMING_SNAKE_CASE							   =featuremaps[i_map]
            _SCREAMING_SNAKE_CASE							   =[]
            for i_focus in range(0							, _a							, _a			):
                 for j_focus in range(0							, _a							, _a			):
                      _SCREAMING_SNAKE_CASE							   =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			)			)
            _SCREAMING_SNAKE_CASE							   =np.asmatrix(_a			).reshape(_a							, _a			)
            featuremap_pooled.append(_a			)
       return featuremap_pooled





  def 						A  (		self				:     Tuple							, _a				:     Dict			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[]
       for i in range(len(_a			)			):
            _SCREAMING_SNAKE_CASE							   =np.shape(data[i]			)
            _SCREAMING_SNAKE_CASE							   =data[i].reshape(1							, shapes[0] * shapes[1]			)
            _SCREAMING_SNAKE_CASE							   =data_listed.getA().tolist()[0]
            data_expanded.extend(_a			)
       _SCREAMING_SNAKE_CASE							   =np.asarray(_a			)
       return data_expanded





  def 						A  (		self				:     Optional[int]							, _a				:     Any			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =np.asarray(_a			)
       _SCREAMING_SNAKE_CASE							   =np.shape(_a			)
       _SCREAMING_SNAKE_CASE							   =data_mat.reshape(1							, shapes[0] * shapes[1]			)
       return data_expanded





  def 						A  (		self				:     List[Any]							, _a				:     Any							, _a				:     Union[str, Any]							, _a				:     Optional[Any]							, _a				:     int							, _a				:     List[str]			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[]
       _SCREAMING_SNAKE_CASE							   =0
       for i_map in range(_a			):
            _SCREAMING_SNAKE_CASE							   =np.ones((size_map, size_map)			)
            for i in range(0							, _a							, _a			):
                 for j in range(0							, _a							, _a			):
                      _SCREAMING_SNAKE_CASE							   =pd_pool[
                          i_pool
                      ]
                      _SCREAMING_SNAKE_CASE							   =i_pool + 1
            _SCREAMING_SNAKE_CASE							   =np.multiply(
                _a							, np.multiply(out_map[i_map]							, (1 - out_map[i_map])			)			)
            pd_all.append(_a			)
       return pd_all





  def 						A  (		self				:     Optional[Any]							, _a				:     Any							, _a				:     Dict							, _a				:     List[Any]							, _a				:     Union[str, Any]							, _a				:     int							, _a				:     str=bool			)     ->   Optional[Any]:





       '''simple docstring'''






       print('----------------------Start Training-------------------------'			)
       print((' - - Shape: Train_Data  ', np.shape(_a			))			)
       print((' - - Shape: Teach_Data  ', np.shape(_a			))			)
       _SCREAMING_SNAKE_CASE							   =0
       _SCREAMING_SNAKE_CASE							   =[]
       _SCREAMING_SNAKE_CASE							   =1_0000
       while rp < n_repeat and mse >= error_accuracy:
            _SCREAMING_SNAKE_CASE							   =0
            print(f"-------------Learning Time {rp}--------------"			)
            for p in range(len(_a			)			):
                 # print('------------Learning Image: %d--------------'%p)
                 _SCREAMING_SNAKE_CASE							   =np.asmatrix(datas_train[p]			)
                 _SCREAMING_SNAKE_CASE							   =np.asarray(datas_teach[p]			)
                 _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.convolute(
                     _a							, self.conva							, self.w_conva							, self.thre_conva							, conv_step=self.step_conva							, )
                 _SCREAMING_SNAKE_CASE							   =self.pooling(_a							, self.size_poolinga			)
                 _SCREAMING_SNAKE_CASE							   =np.shape(_a			)
                 _SCREAMING_SNAKE_CASE							   =self._expand(_a			)
                 _SCREAMING_SNAKE_CASE							   =data_bp_input

                 _SCREAMING_SNAKE_CASE							   =np.dot(_a							, self.vji.T			) - self.thre_bpa
                 _SCREAMING_SNAKE_CASE							   =self.sig(_a			)
                 _SCREAMING_SNAKE_CASE							   =np.dot(_a							, self.wkj.T			) - self.thre_bpa
                 _SCREAMING_SNAKE_CASE							   =self.sig(_a			)

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

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

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

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

       def draw_error():
            _SCREAMING_SNAKE_CASE							   =[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 						A  (		self				:     int							, _a				:     Optional[Any]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[]
       print('-------------------Start Testing-------------------------'			)
       print((' - - Shape: Test_Data  ', np.shape(_a			))			)
       for p in range(len(_a			)			):
            _SCREAMING_SNAKE_CASE							   =np.asmatrix(datas_test[p]			)
            _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.convolute(
                _a							, self.conva							, self.w_conva							, self.thre_conva							, conv_step=self.step_conva							, )
            _SCREAMING_SNAKE_CASE							   =self.pooling(_a							, self.size_poolinga			)
            _SCREAMING_SNAKE_CASE							   =self._expand(_a			)

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





  def 						A  (		self				:     Optional[int]							, _a				:     Optional[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =np.asmatrix(_a			)
       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.convolute(
           _a							, self.conva							, self.w_conva							, self.thre_conva							, conv_step=self.step_conva							, )
       _SCREAMING_SNAKE_CASE							   =self.pooling(_a							, self.size_poolinga			)

       return data_conveda, data_pooleda


if __name__ == "__main__":
     pass



'''simple docstring'''
import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
