
"""simple docstring"""




import pickle

import numpy as np
from matplotlib import pyplot as plt







class   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=0.2 ,			__UpperCamelCase=0.2  )->					str:

          '''simple docstring'''





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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:

          '''simple docstring'''





          UpperCAmelCase__							: Union[str, 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(__UpperCamelCase ,			"wb"  ) as f:
               pickle.dump(__UpperCamelCase ,			__UpperCamelCase  )

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



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase  )->					Dict:

          '''simple docstring'''





          with open(__UpperCamelCase ,			"rb"  ) as f:
               UpperCAmelCase__							: Union[str, Any] 							=						pickle.load(__UpperCamelCase  )  # noqa: S301

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:

          '''simple docstring'''





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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:

          '''simple docstring'''





          return round(__UpperCamelCase ,			3  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Tuple:

          '''simple docstring'''





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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase="average_pool"  )->					Tuple:

          '''simple docstring'''





          UpperCAmelCase__							: List[Any] 							=						len(featuremaps[0]  )
          UpperCAmelCase__							: Dict 							=						int(size_map / size_pooling  )
          UpperCAmelCase__							: Dict 							=						[]
          for i_map in range(len(__UpperCamelCase  )  ):
               UpperCAmelCase__							: Optional[Any] 							=						featuremaps[i_map]
               UpperCAmelCase__							: List[str] 							=						[]
               for i_focus in range(0 ,			__UpperCamelCase ,			__UpperCamelCase  ):
                    for j_focus in range(0 ,			__UpperCamelCase ,			__UpperCamelCase  ):
                         UpperCAmelCase__							: List[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  )  )
               UpperCAmelCase__							: Dict 							=						np.asmatrix(__UpperCamelCase  ).reshape(__UpperCamelCase ,			__UpperCamelCase  )
               featuremap_pooled.append(__UpperCamelCase  )
          return featuremap_pooled



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:

          '''simple docstring'''





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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:

          '''simple docstring'''





          UpperCAmelCase__							: Tuple 							=						np.asarray(__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						np.shape(__UpperCamelCase  )
          UpperCAmelCase__							: Union[str, Any] 							=						data_mat.reshape(1 ,			shapes[0] * shapes[1]  )
          return data_expanded



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[Any]:

          '''simple docstring'''





          UpperCAmelCase__							: List[str] 							=						[]
          UpperCAmelCase__							: Tuple 							=						0
          for i_map in range(__UpperCamelCase  ):
               UpperCAmelCase__							: str 							=						np.ones((size_map, size_map)  )
               for i in range(0 ,			__UpperCamelCase ,			__UpperCamelCase  ):
                    for j in range(0 ,			__UpperCamelCase ,			__UpperCamelCase  ):
                         UpperCAmelCase__							: List[Any] 							=						pd_pool[
                             i_pool
                         ]
                         UpperCAmelCase__							: str 							=						i_pool + 1
               UpperCAmelCase__							: Optional[Any] 							=						np.multiply(
                   __UpperCamelCase ,			np.multiply(out_map[i_map] ,			(1 - out_map[i_map])  )  )
               pd_all.append(__UpperCamelCase  )
          return pd_all



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=bool  )->					Optional[int]:

          '''simple docstring'''





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

                    UpperCAmelCase__							: str 							=						np.dot(__UpperCamelCase ,			self.vji.T  ) - self.thre_bpa
                    UpperCAmelCase__							: Dict 							=						self.sig(__UpperCamelCase  )
                    UpperCAmelCase__							: List[Any] 							=						np.dot(__UpperCamelCase ,			self.wkj.T  ) - self.thre_bpa
                    UpperCAmelCase__							: Optional[int] 							=						self.sig(__UpperCamelCase  )

                    # --------------Model Leaning ------------------------
                    # calculate error and gradient---------------
                    UpperCAmelCase__							: List[str] 							=						np.multiply(
                        (data_teach - bp_outa) ,			np.multiply(__UpperCamelCase ,			(1 - bp_outa)  )  )
                    UpperCAmelCase__							: Tuple 							=						np.multiply(
                        np.dot(__UpperCamelCase ,			self.wkj  ) ,			np.multiply(__UpperCamelCase ,			(1 - bp_outa)  )  )
                    UpperCAmelCase__							: int 							=						np.dot(__UpperCamelCase ,			self.vji  )

                    UpperCAmelCase__							: str 							=						pd_i_all / (self.size_poolinga * self.size_poolinga)
                    UpperCAmelCase__							: List[Any] 							=						pd_conva_pooled.T.getA().tolist()
                    UpperCAmelCase__							: str 							=						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]  ):
                         UpperCAmelCase__							: Any 							=						self._expand_mat(pd_conva_all[k_conv]  )
                         UpperCAmelCase__							: Optional[int] 							=						self.rate_weight * np.dot(__UpperCamelCase ,			__UpperCamelCase  )

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

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

          def draw_error():
               UpperCAmelCase__							: Optional[int] 							=						[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  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:

          '''simple docstring'''





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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:

          '''simple docstring'''





          UpperCAmelCase__							: Any 							=						np.asmatrix(__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.convolute(
              __UpperCamelCase ,			self.conva ,			self.w_conva ,			self.thre_conva ,			conv_step=self.step_conva ,			)
          UpperCAmelCase__							: Dict 							=						self.pooling(__UpperCamelCase ,			self.size_poolinga  )

          return data_conveda, data_pooleda


if __name__ == "__main__":
    pass





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



