



'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
    from ...tokenization_utils_base import TextInput
from ...utils import logging


A__    :   str           =					logging.get_logger(__name__)

A__    :   List[Any]           =					{'''vocab_file''': '''spiece.model'''}

A__    :   List[Any]           =					{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__    :   Optional[int]           =					{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

A__    :   List[str]           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							str      ,     __a						:							Dict      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							int="<unk>"      ,     __a						:							Optional[int]="<pad>"      ,     __a						:							Optional[Any]=100      ,     __a						:							Optional[int]=None      ,     __a						:							Optional[Dict[str, Any]] = None      ,     __a						:							Tuple=True      ,     **__a						:							Optional[Any]      ,     )      ->   None:







       '''simple docstring'''
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
             __snake_case :   int													=    [f'''<extra_id_{i}>''' for i in range(__a  )]
       elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra_id special tokens
             __snake_case :   Tuple													=    len(set(filter(lambda __a  : bool('extra_id' in str(__a  )  )      ,     __a  )  )  )
             if extra_tokens != extra_ids:
                   raise ValueError(
                       f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                       ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                       ' tokens'  )
       if legacy:
             logger.warning_once(
                 f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                 ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'  )

       __snake_case :   Any													=    legacy
       __snake_case :   Dict													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=__a      ,     unk_token=__a      ,     pad_token=__a      ,     extra_ids=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     legacy=__a      ,     **__a      ,     )

       __snake_case :   Tuple													=    vocab_file
       __snake_case :   int													=    extra_ids

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(__a  )
 @staticmethod
 def A_   (   __a						:							Any      ,     __a						:							Any      ,     __a						:							Union[str, Any]  )      ->   Any:







       '''simple docstring'''
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
             __snake_case :   Tuple													=    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                   return init_max_model_length
             elif init_max_model_length is None:
                   warnings.warn(
                       'This tokenizer was incorrectly instantiated with a model max length of'
                       f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                       ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                       ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                       f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                       f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                       f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                       ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                       ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,     __a      ,     )

       return max_model_length
 @property
 def A_   (   self						:							Optional[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.sp_model.get_piece_size() + self._extra_ids
 def A_   (   self						:							str  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   List[str]													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       # normal case: some special tokens
       if token_ids_a is None:
             return ([0] * len(__a  )) + [1]
       return ([0] * len(__a  )) + [1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							Optional[int]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return list(
           set(filter(lambda __a  : bool(re.search(r'<extra_id_\d+>'      ,     __a  )  ) is not None      ,     self.additional_special_tokens  )  )  )
 def A_   (   self						:							int  )      ->   Union[str, Any]:







       '''simple docstring'''
       return [self._convert_token_to_id(__a  ) for token in self.get_sentinel_tokens()]
 def A_   (   self						:							Union[str, Any]      ,     __a						:							List[int]  )      ->   List[int]:







       '''simple docstring'''
       if len(__a  ) > 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 A_   (   self						:							str      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   int													=    [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 A_   (   self						:							Any      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self._add_eos_if_not_present(__a  )
       if token_ids_a is None:
             return token_ids_a
       else:
             __snake_case :   int													=    self._add_eos_if_not_present(__a  )
             return token_ids_a + token_ids_a
 def __getstate__(   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   int													=    self.__dict__.copy()
       __snake_case :   Union[str, Any]													=    None
       return state
 def __setstate__(   self						:							Optional[int]      ,     __a						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   str													=    {}

       __snake_case :   List[str]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )
 def A_   (   self						:							int      ,     __a						:							"TextInput"      ,     **__a						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
             __snake_case :   Any													=    SPIECE_UNDERLINE + text.replace(__a      ,     ' '  )
       return super().tokenize(__a      ,     **__a  )
 def A_   (   self						:							Union[str, Any]      ,     __a						:							List[str]      ,     **__a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if not self.legacy:
             __snake_case :   Optional[Any]													=    text.startswith(__a  )
             if is_first:
                   __snake_case :   List[str]													=    text[1:]

       __snake_case :   Union[str, Any]													=    self.sp_model.encode(__a      ,     out_type=__a  )

       if not self.legacy and not is_first and not text.startswith(' '  ) and tokens[0].startswith(__a  ):
             __snake_case :   Union[str, Any]													=    ([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
       return tokens
 def A_   (   self						:							Any      ,     __a						:							Any  )      ->   str:







       '''simple docstring'''
       if token.startswith('<extra_id_'  ):
             __snake_case :   Union[str, Any]													=    re.match(r'<extra_id_(\d+)>'      ,     __a  )
             __snake_case :   str													=    int(match.group(1  )  )
             return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							Tuple  )      ->   Any:







       '''simple docstring'''
       if index < self.sp_model.get_piece_size():
             __snake_case :   Any													=    self.sp_model.IdToPiece(__a  )
       else:
             __snake_case :   List[str]													=    f'''<extra_id_{self.vocab_size - 1 - index}>'''
       return token
 def A_   (   self						:							Optional[int]      ,     __a						:							int  )      ->   Any:







       '''simple docstring'''
       __snake_case :   List[Any]													=    []
       __snake_case :   Optional[Any]													=    ''
       __snake_case :   List[str]													=    False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(__a  ) + token
                   __snake_case :   Optional[Any]													=    True
                   __snake_case :   Optional[int]													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Optional[int]													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()





 def A_   (   self						:							Dict      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Optional[int]													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

       return (out_vocab_file,)








'''simple docstring'''





import pickle

import numpy as np
from matplotlib import pyplot as plt



class  snake_case__						:
 def __init__(   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Dict      ,     __a						:							List[Any]      ,     __a						:							str      ,     __a						:							str      ,     __a						:							List[str]=0.2      ,     __a						:							Any=0.2  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Any													=    bp_numa
       __snake_case :   str													=    bp_numa
       __snake_case :   Optional[Any]													=    bp_numa
       __snake_case :   Any													=    conva_get[:2]
       __snake_case :   Dict													=    conva_get[2]
       __snake_case :   Optional[int]													=    size_pa
       __snake_case :   str													=    rate_w
       __snake_case :   Optional[Any]													=    rate_t
       __snake_case :   Optional[Any]													=    [
           np.mat(-1 * np.random.rand(self.conva[0]      ,     self.conva[0]  ) + 0.5  )
           for i in range(self.conva[1]  )
       ]
       __snake_case :   List[Any]													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Any													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Optional[int]													=    -2 * np.random.rand(self.conva[1]  ) + 1
       __snake_case :   Optional[Any]													=    -2 * np.random.rand(self.num_bpa  ) + 1
       __snake_case :   str													=    -2 * np.random.rand(self.num_bpa  ) + 1
 def A_   (   self						:							int      ,     __a						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       # save model dict with pickle
       __snake_case :   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(__a      ,     'wb'  ) as f:
             pickle.dump(__a      ,     __a  )

       print(f'''Model saved： {save_path}'''  )
 @classmethod
 def A_   (   cls						:							List[Any]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # read saved model
       with open(__a      ,     'rb'  ) as f:
             __snake_case :   Dict													=    pickle.load(__a  )  # noqa: S301

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







       '''simple docstring'''
       return 1 / (1 + np.exp(-1 * x  ))
 def A_   (   self						:							Any      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       return round(__a      ,     3  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Dict      ,     __a						:							Optional[int]      ,     __a						:							List[str]  )      ->   str:







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

       # expanding the data slice to One dimenssion
       __snake_case :   Union[str, Any]													=    []
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(__a  )  )
       __snake_case :   List[Any]													=    np.asarray(__a  )
       return focus_list, data_featuremap
 def A_   (   self						:							Any      ,     __a						:							int      ,     __a						:							Tuple      ,     __a						:							List[Any]="average_pool"  )      ->   Dict:







       '''simple docstring'''
       # pooling process
       __snake_case :   List[str]													=    len(featuremaps[0]  )
       __snake_case :   Tuple													=    int(size_map / size_pooling  )
       __snake_case :   int													=    []
       for i_map in range(len(__a  )  ):
             __snake_case :   str													=    featuremaps[i_map]
             __snake_case :   Optional[Any]													=    []
             for i_focus in range(0      ,     __a      ,     __a  ):
                   for j_focus in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Dict													=    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  )  )
             __snake_case :   List[str]													=    np.asmatrix(__a  ).reshape(__a      ,     __a  )
             featuremap_pooled.append(__a  )
       return featuremap_pooled
 def A_   (   self						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   int:







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







       '''simple docstring'''
       # expanding matrix to one dimension list
       __snake_case :   int													=    np.asarray(__a  )
       __snake_case :   str													=    np.shape(__a  )
       __snake_case :   Any													=    data_mat.reshape(1      ,     shapes[0] * shapes[1]  )
       return data_expanded
 def A_   (   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							int      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    []
       __snake_case :   Tuple													=    0
       for i_map in range(__a  ):
             __snake_case :   Union[str, Any]													=    np.ones((size_map, size_map)  )
             for i in range(0      ,     __a      ,     __a  ):
                   for j in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Any													=    pd_pool[
                             i_pool
                         ]
                         __snake_case :   List[Any]													=    i_pool + 1
             __snake_case :   List[Any]													=    np.multiply(
                 __a      ,     np.multiply(out_map[i_map]      ,     (1 - out_map[i_map])  )  )
             pd_all.append(__a  )
       return pd_all
 def A_   (   self						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Dict      ,     __a						:							Tuple      ,     __a						:							Optional[int]=bool  )      ->   List[Any]:







       '''simple docstring'''
       # model traning
       print('----------------------Start Training-------------------------'  )
       print((' - - Shape: Train_Data  ', np.shape(__a  ))  )
       print((' - - Shape: Teach_Data  ', np.shape(__a  ))  )
       __snake_case :   str													=    0
       __snake_case :   List[str]													=    []
       __snake_case :   List[Any]													=    10000
       while rp < n_repeat and mse >= error_accuracy:
             __snake_case :   int													=    0
             print(f'''-------------Learning Time {rp}--------------'''  )
             for p in range(len(__a  )  ):
                   # print('------------Learning Image: %d--------------'%p)
                   __snake_case :   List[Any]													=    np.asmatrix(datas_train[p]  )
                   __snake_case :   Optional[Any]													=    np.asarray(datas_teach[p]  )
                   __snake_case							,     __snake_case :   List[Any]													=    self.convolute(
                       __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
                   __snake_case :   Tuple													=    self.pooling(__a      ,     self.size_poolinga  )
                   __snake_case :   Dict													=    np.shape(__a  )
                   __snake_case :   Tuple													=    self._expand(__a  )
                   __snake_case :   str													=    data_bp_input

                   __snake_case :   List[Any]													=    np.dot(__a      ,     self.vji.T  ) - self.thre_bpa
                   __snake_case :   Any													=    self.sig(__a  )
                   __snake_case :   Tuple													=    np.dot(__a      ,     self.wkj.T  ) - self.thre_bpa
                   __snake_case :   Optional[Any]													=    self.sig(__a  )

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

                   __snake_case :   Optional[int]													=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                   __snake_case :   Tuple													=    pd_conva_pooled.T.getA().tolist()
                   __snake_case :   Optional[Any]													=    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]  ):
                         __snake_case :   int													=    self._expand_mat(pd_conva_all[k_conv]  )
                         __snake_case :   Optional[int]													=    self.rate_weight * np.dot(__a      ,     __a  )

                         __snake_case :   List[Any]													=    self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])  )

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

       def draw_error():
             __snake_case :   Union[str, Any]													=    [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 A_   (   self						:							Tuple      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







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

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





 def A_   (   self						:							Optional[Any]      ,     __a						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       # return the data of image after convoluting process so we can check it out
       __snake_case :   int													=    np.asmatrix(__a  )
       __snake_case							,     __snake_case :   int													=    self.convolute(
           __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
       __snake_case :   Dict													=    self.pooling(__a      ,     self.size_poolinga  )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass



