




import pickle

import numpy as np
from matplotlib import pyplot as plt

class     __a:






    """simple docstring"""





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						int:
         # save model dict with pickle
         UpperCAmelCase_     :				Dict  =     {
             '''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(_SCREAMING_SNAKE_CASE  ,'''wb'''    ) as f:
              pickle.dump(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

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





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         # read saved model
         with open(_SCREAMING_SNAKE_CASE  ,'''rb'''    ) as f:
              UpperCAmelCase_     :				Tuple  =     pickle.load(_SCREAMING_SNAKE_CASE    )  # noqa: S301

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						List[Any]:
         return 1 / (1 + np.exp(-1 * x    ))





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         return round(_SCREAMING_SNAKE_CASE  ,3    )





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

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE="average_pool"    )					->						Any:
         # pooling process
         UpperCAmelCase_     :				Dict  =     len(featuremaps[0]    )
         UpperCAmelCase_     :				Optional[Any]  =     int(size_map / size_pooling    )
         UpperCAmelCase_     :				Optional[Any]  =     []
         for i_map in range(len(_SCREAMING_SNAKE_CASE    )    ):
              UpperCAmelCase_     :				Optional[Any]  =     featuremaps[i_map]
              UpperCAmelCase_     :				int  =     []
              for i_focus in range(0  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                   for j_focus in range(0  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                        UpperCAmelCase_     :				int  =     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(_SCREAMING_SNAKE_CASE    )    )
                        elif pooling_type == "max_pooling":
                             # max pooling
                             map_pooled.append(np.max(_SCREAMING_SNAKE_CASE    )    )
              UpperCAmelCase_     :				Tuple  =     np.asmatrix(_SCREAMING_SNAKE_CASE    ).reshape(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              featuremap_pooled.append(_SCREAMING_SNAKE_CASE    )
         return featuremap_pooled





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         # expanding three dimension data to one dimension list
         UpperCAmelCase_     :				Dict  =     []
         for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
              UpperCAmelCase_     :				Optional[Any]  =     np.shape(data[i]    )
              UpperCAmelCase_     :				List[str]  =     data[i].reshape(1  ,shapes[0] * shapes[1]    )
              UpperCAmelCase_     :				Tuple  =     data_listed.getA().tolist()[0]
              data_expanded.extend(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     np.asarray(_SCREAMING_SNAKE_CASE    )
         return data_expanded





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





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=bool    )					->						str:
         # model traning
         print('''----------------------Start Training-------------------------'''    )
         print((''' - - Shape: Train_Data  ''', np.shape(_SCREAMING_SNAKE_CASE    ))    )
         print((''' - - Shape: Teach_Data  ''', np.shape(_SCREAMING_SNAKE_CASE    ))    )
         UpperCAmelCase_     :				List[str]  =     0
         UpperCAmelCase_     :				Optional[Any]  =     []
         UpperCAmelCase_     :				Union[str, Any]  =     10_000
         while rp < n_repeat and mse >= error_accuracy:
              UpperCAmelCase_     :				Dict  =     0
              print(f'''-------------Learning Time {rp}--------------'''    )
              for p in range(len(_SCREAMING_SNAKE_CASE    )    ):
                   # print('------------Learning Image: %d--------------'%p)
                   UpperCAmelCase_     :				Optional[int]  =     np.asmatrix(datas_train[p]    )
                   UpperCAmelCase_     :				Tuple  =     np.asarray(datas_teach[p]    )
                   UpperCAmelCase_,       UpperCAmelCase_     :				str  =     self.convolute(
                       _SCREAMING_SNAKE_CASE  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
                   UpperCAmelCase_     :				List[Any]  =     self.pooling(_SCREAMING_SNAKE_CASE  ,self.size_poolinga    )
                   UpperCAmelCase_     :				Optional[int]  =     np.shape(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Tuple  =     self._expand(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Tuple  =     data_bp_input

                   UpperCAmelCase_     :				Union[str, Any]  =     np.dot(_SCREAMING_SNAKE_CASE  ,self.vji.T    ) - self.thre_bpa
                   UpperCAmelCase_     :				Union[str, Any]  =     self.sig(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Dict  =     np.dot(_SCREAMING_SNAKE_CASE  ,self.wkj.T    ) - self.thre_bpa
                   UpperCAmelCase_     :				Dict  =     self.sig(_SCREAMING_SNAKE_CASE    )

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

                   UpperCAmelCase_     :				Any  =     pd_i_all / (self.size_poolinga * self.size_poolinga)
                   UpperCAmelCase_     :				List[Any]  =     pd_conva_pooled.T.getA().tolist()
                   UpperCAmelCase_     :				int  =     self._calculate_gradient_from_pool(
                       _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,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_     :				int  =     self._expand_mat(pd_conva_all[k_conv]    )
                        UpperCAmelCase_     :				str  =     self.rate_weight * np.dot(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

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

         def draw_error():
              UpperCAmelCase_     :				List[str]  =     [error_accuracy for i in range(int(n_repeat * 1.2    )    )]
              plt.plot(_SCREAMING_SNAKE_CASE  ,'''+-'''    )
              plt.plot(_SCREAMING_SNAKE_CASE  ,'''r--'''    )
              plt.xlabel('''Learning Times'''    )
              plt.ylabel('''All_mse'''    )
              plt.grid(_SCREAMING_SNAKE_CASE  ,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  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         # model predict
         UpperCAmelCase_     :				str  =     []
         print('''-------------------Start Testing-------------------------'''    )
         print((''' - - Shape: Test_Data  ''', np.shape(_SCREAMING_SNAKE_CASE    ))    )
         for p in range(len(_SCREAMING_SNAKE_CASE    )    ):
              UpperCAmelCase_     :				List[str]  =     np.asmatrix(datas_test[p]    )
              UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.convolute(
                  _SCREAMING_SNAKE_CASE  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
              UpperCAmelCase_     :				Dict  =     self.pooling(_SCREAMING_SNAKE_CASE  ,self.size_poolinga    )
              UpperCAmelCase_     :				Tuple  =     self._expand(_SCREAMING_SNAKE_CASE    )

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         # return the data of image after convoluting process so we can check it out
         UpperCAmelCase_     :				Union[str, Any]  =     np.asmatrix(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.convolute(
             _SCREAMING_SNAKE_CASE  ,self.conva  ,self.w_conva  ,self.thre_conva  ,conv_step=self.step_conva  ,)
         UpperCAmelCase_     :				str  =     self.pooling(_SCREAMING_SNAKE_CASE  ,self.size_poolinga    )

         return data_conveda, data_pooleda


if __name__ == "__main__":
     pass





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)