


import pickle

import numpy as np
from matplotlib import pyplot as plt

class     UpperCAmelCase__ :



     '''simple docstring'''




     def __init__(			self							:							Any						,   UpperCamelCase							:							List[Any]						,   UpperCamelCase							:							Any						,   UpperCamelCase							:							str						,   UpperCamelCase							:							List[Any]						,   UpperCamelCase							:							Dict						,   UpperCamelCase							:							Dict=0.2						,   UpperCamelCase							:							Dict=0.2		):





            """simple docstring"""



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




     def 	lowerCAmelCase_ (			self							:							str						,   UpperCamelCase							:							str		):





            """simple docstring"""



            _lowercase       :					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(UpperCamelCase						,   '''wb'''		) as f:
                   pickle.dump(UpperCamelCase						,   UpperCamelCase		)

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




     @classmethod
     def 	lowerCAmelCase_ (			cls							:							str						,   UpperCamelCase							:							Dict		):





            """simple docstring"""



            with open(UpperCamelCase						,   '''rb'''		) as f:
                   _lowercase       :					Any     							=      pickle.load(UpperCamelCase		)  # noqa: S301

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




     def 	lowerCAmelCase_ (			self							:							Any						,   UpperCamelCase							:							Optional[Any]		):





            """simple docstring"""



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




     def 	lowerCAmelCase_ (			self							:							List[str]						,   UpperCamelCase							:							Union[str, Any]		):





            """simple docstring"""



            return round(UpperCamelCase						,   3		)




     def 	lowerCAmelCase_ (			self							:							str						,   UpperCamelCase							:							Union[str, Any]						,   UpperCamelCase							:							List[Any]						,   UpperCamelCase							:							List[Any]						,   UpperCamelCase							:							Any						,   UpperCamelCase							:							Tuple		):





            """simple docstring"""



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

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




     def 	lowerCAmelCase_ (			self							:							List[Any]						,   UpperCamelCase							:							Optional[Any]						,   UpperCamelCase							:							Dict						,   UpperCamelCase							:							List[Any]="average_pool"		):





            """simple docstring"""



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




     def 	lowerCAmelCase_ (			self							:							List[str]						,   UpperCamelCase							:							Dict		):





            """simple docstring"""



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




     def 	lowerCAmelCase_ (			self							:							List[str]						,   UpperCamelCase							:							Tuple		):





            """simple docstring"""



            _lowercase       :					Optional[Any]     							=      np.asarray(UpperCamelCase		)
            _lowercase       :					Any     							=      np.shape(UpperCamelCase		)
            _lowercase       :					Optional[int]     							=      data_mat.reshape(1						,   shapes[0] * shapes[1]		)
            return data_expanded




     def 	lowerCAmelCase_ (			self							:							Union[str, Any]						,   UpperCamelCase							:							Union[str, Any]						,   UpperCamelCase							:							Tuple						,   UpperCamelCase							:							Tuple						,   UpperCamelCase							:							Dict						,   UpperCamelCase							:							Dict		):





            """simple docstring"""



            _lowercase       :					int     							=      []
            _lowercase       :					str     							=      0
            for i_map in range(UpperCamelCase		):
                   _lowercase       :					Optional[int]     							=      np.ones((size_map, size_map)		)
                   for i in range(0						,   UpperCamelCase						,   UpperCamelCase		):
                          for j in range(0						,   UpperCamelCase						,   UpperCamelCase		):
                                 _lowercase       :					Tuple     							=      pd_pool[
                                     i_pool
                                 ]
                                 _lowercase       :					str     							=      i_pool + 1
                   _lowercase       :					Union[str, 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							:							Union[str, Any]						,   UpperCamelCase							:							str						,   UpperCamelCase							:							str						,   UpperCamelCase							:							int						,   UpperCamelCase							:							Union[str, Any]						,   UpperCamelCase							:							Optional[Any]						,   UpperCamelCase							:							Dict=bool		):





            """simple docstring"""



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

                          _lowercase       :					int     							=      np.dot(UpperCamelCase						,   self.vji.T		) - self.thre_bpa
                          _lowercase       :					Any     							=      self.sig(UpperCamelCase		)
                          _lowercase       :					int     							=      np.dot(UpperCamelCase						,   self.wkj.T		) - self.thre_bpa
                          _lowercase       :					List[Any]     							=      self.sig(UpperCamelCase		)

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

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

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

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

            def draw_error():
                   _lowercase       :					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							:							Tuple						,   UpperCamelCase							:							Dict		):





            """simple docstring"""



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

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




     def 	lowerCAmelCase_ (			self							:							int						,   UpperCamelCase							:							Union[str, Any]		):





            """simple docstring"""



            _lowercase       :					List[Any]     							=      np.asmatrix(UpperCamelCase		)
            _lowercase       :					Union[str, Any]     							=      self.convolute(
                UpperCamelCase						,   self.conva						,   self.w_conva						,   self.thre_conva						,   conv_step=self.step_conva						,   )
            _lowercase       :					Optional[Any]     							=      self.pooling(UpperCamelCase						,   self.size_poolinga		)

            return data_conveda, data_pooleda


if __name__ == "__main__":
   pass

'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase     :							int       						=     logging.get_logger(__name__)

lowercase     :							Dict       						=     {'vocab_file': 'spiece.model'}

lowercase     :							Tuple       						=     {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowercase     :							Tuple       						=     {'bert_for_seq_generation': 5_12}





class 					A							(				__snake_case		):
     __magic_name__          =						VOCAB_FILES_NAMES
     __magic_name__          =						PRETRAINED_VOCAB_FILES_MAP
     __magic_name__          =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__          =						[]
     __magic_name__          =						['''input_ids''', '''attention_mask''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE="<s>"		,     SCREAMING_SNAKE_CASE="</s>"		,     SCREAMING_SNAKE_CASE="<unk>"		,     SCREAMING_SNAKE_CASE="<pad>"		,     SCREAMING_SNAKE_CASE="<::::>"		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE		,     )					->							None:






         """simple docstring"""






         A			:  str   =		{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE		,     eos_token=SCREAMING_SNAKE_CASE		,     unk_token=SCREAMING_SNAKE_CASE		,     pad_token=SCREAMING_SNAKE_CASE		,     sep_token=SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     **SCREAMING_SNAKE_CASE		,     )

         A			:  List[Any]   =		vocab_file

         A			:  List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.sp_model.get_piece_size()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
         vocab.update(self.added_tokens_encoder      )
         return vocab





     def __getstate__(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Dict   =		self.__dict__.copy()
         A			:  int   =		None
         return state





     def __setstate__(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		d

         # for backward compatibility
         if not hasattr(self		,     '''sp_model_kwargs'''      ):
             A			:  Optional[int]   =		{}

         A			:  Optional[int]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(self.vocab_file      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






         return self.sp_model.encode(SCREAMING_SNAKE_CASE		,     out_type=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Tuple   =		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE      )
         return token





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  List[Any]   =		[]
         A			:  Dict   =		''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      ) + token
                 A			:  str   =		[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
         return out_string.strip()





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None      )					->							Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
             return
         A			:  int   =		os.path.join(
             SCREAMING_SNAKE_CASE		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

         if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file		,     SCREAMING_SNAKE_CASE      )
         elif not os.path.isfile(self.vocab_file      ):
             with open(SCREAMING_SNAKE_CASE		,     '''wb'''      ) as fi:
                 A			:  str   =		self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE      )

         return (out_vocab_file,)




