
'''simple docstring'''






import pickle

import numpy as np
from matplotlib import pyplot as plt

class 			UpperCAmelCase__    :



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



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							int  ):
          # save model dict with pickle
          _lowerCamelCase					: 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(__A,"wb"  ) as f:
                pickle.dump(__A,__A  )

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



    @classmethod
    def 	lowerCamelCase_	(						cls   :							Any,__A   :							Dict  ):
          # read saved model
          with open(__A,"rb"  ) as f:
                _lowerCamelCase					: List[str]		    = pickle.load(__A  )  # noqa: S301

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



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							Optional[Any]  ):
          return 1 / (1 + np.exp(-1 * x  ))



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							str  ):
          return round(__A,3  )



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

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



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



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



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							Optional[int]  ):
          # expanding matrix to one dimension list
          _lowerCamelCase					: int		    = np.asarray(__A  )
          _lowerCamelCase					: List[str]		    = np.shape(__A  )
          _lowerCamelCase					: Tuple		    = data_mat.reshape(1,shapes[0] * shapes[1]  )
          return data_expanded



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



    def 	lowerCamelCase_	(						self   :							Union[str, Any],__A   :							Dict,__A   :							Optional[Any],__A   :							Union[str, Any],__A   :							Optional[Any],__A   :							Union[str, Any],__A   :							Tuple=bool  ):
          # model traning
          print("----------------------Start Training-------------------------"  )
          print((" - - Shape: Train_Data  ", np.shape(__A  ))  )
          print((" - - Shape: Teach_Data  ", np.shape(__A  ))  )
          _lowerCamelCase					: Tuple		    = 0
          _lowerCamelCase					: Optional[Any]		    = []
          _lowerCamelCase					: List[str]		    = 1_0_0_0_0
          while rp < n_repeat and mse >= error_accuracy:
                _lowerCamelCase					: List[Any]		    = 0
                print(f'-------------Learning Time {rp}--------------'  )
                for p in range(len(__A  )  ):
                      # print('------------Learning Image: %d--------------'%p)
                      _lowerCamelCase					: List[str]		    = np.asmatrix(datas_train[p]  )
                      _lowerCamelCase					: Dict		    = np.asarray(datas_teach[p]  )
                      _lowerCamelCase    ,		_lowerCamelCase					: int		    = self.convolute(
                          __A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
                      _lowerCamelCase					: int		    = self.pooling(__A,self.size_poolinga  )
                      _lowerCamelCase					: List[str]		    = np.shape(__A  )
                      _lowerCamelCase					: Optional[Any]		    = self._expand(__A  )
                      _lowerCamelCase					: List[str]		    = data_bp_input

                      _lowerCamelCase					: Union[str, Any]		    = np.dot(__A,self.vji.T  ) - self.thre_bpa
                      _lowerCamelCase					: Optional[int]		    = self.sig(__A  )
                      _lowerCamelCase					: Union[str, Any]		    = np.dot(__A,self.wkj.T  ) - self.thre_bpa
                      _lowerCamelCase					: List[Any]		    = self.sig(__A  )

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

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

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

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

          def draw_error():
                _lowerCamelCase					: List[str]		    = [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 	lowerCamelCase_	(						self   :							int,__A   :							List[Any]  ):
          # model predict
          _lowerCamelCase					: Any		    = []
          print("-------------------Start Testing-------------------------"  )
          print((" - - Shape: Test_Data  ", np.shape(__A  ))  )
          for p in range(len(__A  )  ):
                _lowerCamelCase					: Optional[int]		    = np.asmatrix(datas_test[p]  )
                _lowerCamelCase    ,		_lowerCamelCase					: Union[str, Any]		    = self.convolute(
                    __A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
                _lowerCamelCase					: Optional[int]		    = self.pooling(__A,self.size_poolinga  )
                _lowerCamelCase					: int		    = self._expand(__A  )

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



    def 	lowerCamelCase_	(						self   :							Any,__A   :							str  ):
          # return the data of image after convoluting process so we can check it out
          _lowerCamelCase					: Any		    = np.asmatrix(__A  )
          _lowerCamelCase    ,		_lowerCamelCase					: Optional[int]		    = self.convolute(
              __A,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
          _lowerCamelCase					: Optional[int]		    = self.pooling(__A,self.size_poolinga  )

          return data_conveda, data_pooleda


if __name__ == "__main__":
 pass




'''simple docstring'''





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			CTRLTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Any		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt react readapt apt'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]

      _a           =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



