





import pickle

import numpy as np
from matplotlib import pyplot as plt





class 				A_				:







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[int]  )						->					Optional[int]:
        # save model dict with pickle
        _lowercase							= {
            '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    __UpperCAmelCase (    cls	:	Optional[int]						,__A	:	Tuple  )						->					int:
        # read saved model
        with open(__A						,'rb'  ) as f:
         _lowercase							= pickle.load(__A  )  # noqa: S301

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







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	List[Any]  )						->					Tuple:
        return 1 / (1 + np.exp(-1 * x  ))







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[Any]  )						->					str:
        return round(__A						,3  )







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

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







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, Any]						,__A	:	Optional[int]						,__A	:	Optional[Any]="average_pool"  )						->					Dict:
        # pooling process
        _lowercase							= len(featuremaps[0]  )
        _lowercase							= int(size_map / size_pooling  )
        _lowercase							= []
        for i_map in range(len(__A  )  ):
         _lowercase							= featuremaps[i_map]
         _lowercase							= []
         for i_focus in range(0						,__A						,__A  ):
          for j_focus in range(0						,__A						,__A  ):
           _lowercase							= 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  )  )
         _lowercase							= np.asmatrix(__A  ).reshape(__A						,__A  )
         featuremap_pooled.append(__A  )
        return featuremap_pooled







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Union[str, Any]  )						->					Optional[int]:
        # expanding three dimension data to one dimension list
        _lowercase							= []
        for i in range(len(__A  )  ):
         _lowercase							= np.shape(data[i]  )
         _lowercase							= data[i].reshape(1						,shapes[0] * shapes[1]  )
         _lowercase							= data_listed.getA().tolist()[0]
         data_expanded.extend(__A  )
        _lowercase							= np.asarray(__A  )
        return data_expanded







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







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







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	List[str]						,__A	:	Union[str, Any]						,__A	:	Union[str, Any]						,__A	:	Any						,__A	:	str						,__A	:	Any=bool  )						->					int:
        # model traning
        print('----------------------Start Training-------------------------'  )
        print((' - - Shape: Train_Data  ', np.shape(__A  ))  )
        print((' - - Shape: Teach_Data  ', np.shape(__A  ))  )
        _lowercase							= 0
        _lowercase							= []
        _lowercase							= 1_0000
        while rp < n_repeat and mse >= error_accuracy:
         _lowercase							= 0
         print(F"""-------------Learning Time {rp}--------------"""  )
         for p in range(len(__A  )  ):
          # print('------------Learning Image: %d--------------'%p)
          _lowercase							= np.asmatrix(datas_train[p]  )
          _lowercase							= np.asarray(datas_teach[p]  )
          _lowercase						,		_lowercase							= self.convolute(
              __A						,self.conva						,self.w_conva						,self.thre_conva						,conv_step=self.step_conva						,)
          _lowercase							= self.pooling(__A						,self.size_poolinga  )
          _lowercase							= np.shape(__A  )
          _lowercase							= self._expand(__A  )
          _lowercase							= data_bp_input

          _lowercase							= np.dot(__A						,self.vji.T  ) - self.thre_bpa
          _lowercase							= self.sig(__A  )
          _lowercase							= np.dot(__A						,self.wkj.T  ) - self.thre_bpa
          _lowercase							= self.sig(__A  )

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

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

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

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

        def draw_error():
         _lowercase							= [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    __UpperCAmelCase (    self	:	Tuple						,__A	:	Dict  )						->					List[Any]:
        # model predict
        _lowercase							= []
        print('-------------------Start Testing-------------------------'  )
        print((' - - Shape: Test_Data  ', np.shape(__A  ))  )
        for p in range(len(__A  )  ):
         _lowercase							= np.asmatrix(datas_test[p]  )
         _lowercase						,		_lowercase							= self.convolute(
             __A						,self.conva						,self.w_conva						,self.thre_conva						,conv_step=self.step_conva						,)
         _lowercase							= self.pooling(__A						,self.size_poolinga  )
         _lowercase							= self._expand(__A  )

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







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Tuple:
        # return the data of image after convoluting process so we can check it out
        _lowercase							= np.asmatrix(__A  )
        _lowercase						,		_lowercase							= self.convolute(
            __A						,self.conva						,self.w_conva						,self.thre_conva						,conv_step=self.step_conva						,)
        _lowercase							= self.pooling(__A						,self.size_poolinga  )

        return data_conveda, data_pooleda


if __name__ == "__main__":
      pass






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)