





'''simple docstring'''






import pickle

import numpy as np
from matplotlib import pyplot as plt


class   _lowercase						:


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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       # save model dict with pickle
       A       :       int							=						{
           '''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(__lowercase    ,   '''wb'''					) as f:
           pickle.dump(__lowercase    ,   __lowercase					)

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


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase					):
       # read saved model
       with open(__lowercase    ,   '''rb'''					) as f:
           A       :       Dict							=						pickle.load(__lowercase					)  # noqa: S301

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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       return 1 / (1 + np.exp(-1 * x					))


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       return round(__lowercase    ,   3					)


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

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


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


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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       # expanding matrix to one dimension list
       A       :       int							=						np.asarray(__lowercase					)
       A       :       Optional[Any]							=						np.shape(__lowercase					)
       A       :       Optional[Any]							=						data_mat.reshape(1    ,   shapes[0] * shapes[1]					)
       return data_expanded


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[str]							=						[]
       A       :       Any							=						0
       for i_map in range(__lowercase					):
           A       :       str							=						np.ones((size_map, size_map)					)
           for i in range(0    ,   __lowercase    ,   __lowercase					):
               for j in range(0    ,   __lowercase    ,   __lowercase					):
                   A       :       Tuple							=						pd_pool[
                       i_pool
                   ]
                   A       :       List[str]							=						i_pool + 1
           A       :       List[Any]							=						np.multiply(
               __lowercase    ,   np.multiply(out_map[i_map]    ,   (1 - out_map[i_map])					)					)
           pd_all.append(__lowercase					)
       return pd_all


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

               A       :       Any							=						np.dot(__lowercase    ,   self.vji.T					) - self.thre_bpa
               A       :       int							=						self.sig(__lowercase					)
               A       :       int							=						np.dot(__lowercase    ,   self.wkj.T					) - self.thre_bpa
               A       :       Optional[int]							=						self.sig(__lowercase					)

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

               A       :       Any							=						pd_i_all / (self.size_poolinga * self.size_poolinga)
               A       :       int							=						pd_conva_pooled.T.getA().tolist()
               A       :       int							=						self._calculate_gradient_from_pool(
                   __lowercase    ,   __lowercase    ,   shape_featuremapa[0]    ,   shape_featuremapa[1]    ,   self.size_poolinga    ,   )
               # weight and threshold learning process---------
               # convolution layer
               for k_conv in range(self.conva[1]					):
                   A       :       Union[str, Any]							=						self._expand_mat(pd_conva_all[k_conv]					)
                   A       :       List[str]							=						self.rate_weight * np.dot(__lowercase    ,   __lowercase					)

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

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

       def draw_error():
           A       :       Optional[Any]							=						[error_accuracy for i in range(int(n_repeat * 1.2					)					)]
           plt.plot(__lowercase    ,   '''+-'''					)
           plt.plot(__lowercase    ,   '''r--'''					)
           plt.xlabel('''Learning Times'''					)
           plt.ylabel('''All_mse'''					)
           plt.grid(__lowercase    ,   alpha=0.5					)
           plt.show()

       print('''------------------Training Complished---------------------'''					)
       print((''' - - Training epoch: ''', rp, f'''     - - Mse: {mse:.6f}''')					)
       if draw_e:
           draw_error()
       return mse


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       # model predict
       A       :       List[str]							=						[]
       print('''-------------------Start Testing-------------------------'''					)
       print((''' - - Shape: Test_Data  ''', np.shape(__lowercase					))					)
       for p in range(len(__lowercase					)					):
           A       :       int							=						np.asmatrix(datas_test[p]					)
           A,	A       :       Optional[Any]							=						self.convolute(
               __lowercase    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
           A       :       int							=						self.pooling(__lowercase    ,   self.size_poolinga					)
           A       :       str							=						self._expand(__lowercase					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       # return the data of image after convoluting process so we can check it out
       A       :       List[str]							=						np.asmatrix(__lowercase					)
       A,	A       :       Tuple							=						self.convolute(
           __lowercase    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
       A       :       Optional[int]							=						self.pooling(__lowercase    ,   self.size_poolinga					)

       return data_conveda, data_pooleda


if __name__ == "__main__":
     pass







'''simple docstring'''






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_     =	logging.get_logger(__name__)


class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""input_ids""", """attention_mask"""]


   def __init__( self    ,   _UpperCAmelCase="</s>"    ,   _UpperCAmelCase="<unk>"    ,   _UpperCAmelCase="<pad>"    ,   _UpperCAmelCase=125    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           A       :       str							=						[f'''<extra_id_{i}>''' for i in range(_UpperCAmelCase					)]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           A       :       List[str]							=						len(set(filter(lambda _UpperCAmelCase					: bool('''extra_id''' in str(_UpperCAmelCase					)					)    ,   _UpperCAmelCase					)					)					)
           if extra_tokens != extra_ids:
               raise ValueError(
                   f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                   ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                   ''' extra_ids tokens'''					)

       A       :       str							=						AddedToken(_UpperCAmelCase    ,   lstrip=_UpperCAmelCase    ,   rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) else pad_token
       A       :       Any							=						AddedToken(_UpperCAmelCase    ,   lstrip=_UpperCAmelCase    ,   rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) else eos_token
       A       :       Optional[Any]							=						AddedToken(_UpperCAmelCase    ,   lstrip=_UpperCAmelCase    ,   rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					) else unk_token

       super().__init__(
           eos_token=_UpperCAmelCase    ,   unk_token=_UpperCAmelCase    ,   pad_token=_UpperCAmelCase    ,   extra_ids=_UpperCAmelCase    ,   additional_special_tokens=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )

       A       :       int							=						extra_ids

       A       :       Any							=						2**8  # utf is 8 bits

       # define special tokens dict
       A       :       Dict[int, str]							=						{
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       A       :       Tuple							=						len(self.special_tokens_encoder					)
       A       :       Union[str, Any]							=						len(_UpperCAmelCase					)
       for i, token in enumerate(_UpperCAmelCase					):
           A       :       Optional[Any]							=						self.vocab_size + i - n
       A       :       Dict[str, int]							=						{v: k for k, v in self.special_tokens_encoder.items()}


   @property
   def 					snake_case				( self					):
       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = False					):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase    ,   token_ids_a=_UpperCAmelCase    ,   already_has_special_tokens=_UpperCAmelCase					)

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(_UpperCAmelCase					)) + [1]
       return ([0] * len(_UpperCAmelCase					)) + [1] + ([0] * len(_UpperCAmelCase					)) + [1]


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if len(_UpperCAmelCase					) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
               ''' eos tokens being added.'''					)
           return token_ids
       else:
           return token_ids + [self.eos_token_id]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Optional[Any]							=						[self.eos_token_id]

       if token_ids_a is None:
           return len(token_ids_a + eos					) * [0]
       return len(token_ids_a + eos + token_ids_a + eos					) * [0]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Union[str, Any]							=						self._add_eos_if_not_present(_UpperCAmelCase					)
       if token_ids_a is None:
           return token_ids_a
       else:
           A       :       Tuple							=						self._add_eos_if_not_present(_UpperCAmelCase					)
           return token_ids_a + token_ids_a


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       str							=						[chr(_UpperCAmelCase					) for i in text.encode('''utf-8'''					)]
       return tokens


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if token in self.special_tokens_encoder:
           A       :       Union[str, Any]							=						self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
           A       :       int							=						self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase					) != 1:
           A       :       Union[str, Any]							=						self.unk_token_id
       else:
           A       :       Union[str, Any]							=						ord(_UpperCAmelCase					) + self._num_special_tokens
       return token_id


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       if index in self.special_tokens_decoder:
           A       :       Optional[Any]							=						self.special_tokens_decoder[index]
       else:
           A       :       Dict							=						chr(index - self._num_special_tokens					)
       return token


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       List[str]							=						B''''''
       for token in tokens:
           if token in self.special_tokens_decoder:
               A       :       List[str]							=						self.special_tokens_decoder[token].encode('''utf-8'''					)
           elif token in self.added_tokens_decoder:
               A       :       Optional[int]							=						self.special_tokens_decoder[token].encode('''utf-8'''					)
           elif token in self.special_tokens_encoder:
               A       :       Union[str, Any]							=						token.encode('''utf-8'''					)
           elif token in self.added_tokens_encoder:
               A       :       str							=						token.encode('''utf-8'''					)
           else:
               A       :       Tuple							=						bytes([ord(_UpperCAmelCase					)]					)
           bstring += tok_string
       A       :       List[Any]							=						bstring.decode('''utf-8'''    ,   errors='''ignore'''					)
       return string


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       return ()
