





"""simple docstring"""




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

import sentencepiece as spm

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


lowercase				:	List[Any]      						=  logging.get_logger(__name__)

lowercase				:	Any      						=  """▁"""

lowercase				:	Dict      						=  {"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase				:	int      						=  {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase				:	str      						=  {
    """facebook/mbart-large-50-one-to-many-mmt""": 1_0_2_4,
}

# fmt: off
lowercase				:	Any      						=  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]





class 			A__   (							snake_case_     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  VOCAB_FILES_NAMES
   __A       :    Any	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Any	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    int	  =  ['''input_ids''', '''attention_mask''']

   __A       :    List[str]	  =  []
   __A       :    Any	  =  []

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







          '''simple docstring'''

          a__						:     List[Any]			=    AddedToken(lowercase ,  lstrip=lowercase ,  rstrip=lowercase) if isinstance(lowercase ,  lowercase) else mask_token

          a__						:     Any			=    {} if sp_model_kwargs is None else sp_model_kwargs

          a__						:     Union[str, Any]			=    kwargs.get('additional_special_tokens' ,  [])
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=lowercase ,  tgt_lang=lowercase ,  eos_token=lowercase ,  unk_token=lowercase ,  sep_token=lowercase ,  cls_token=lowercase ,  pad_token=lowercase ,  mask_token=lowercase ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowercase ,  )

          a__						:     List[str]			=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase))
          a__						:     Union[str, Any]			=    vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          a__						:     List[str]			=    {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          a__						:     Union[str, Any]			=    1

          a__						:     Dict			=    len(self.sp_model)
          a__						:     str			=    {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase)
          }
          a__						:     List[Any]			=    {v: k for k, v in self.lang_code_to_id.items()}
          a__						:     Dict			=    len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
          a__						:     Optional[int]			=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}

          a__						:     int			=    src_lang if src_lang is not None else 'en_XX'
          a__						:     int			=    self.lang_code_to_id[self._src_lang]
          a__						:     Union[str, Any]			=    tgt_lang
          self.set_src_lang_special_tokens(self._src_lang)

   @property
   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token

   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          return self._src_lang

   @src_lang.setter
   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     Optional[Any]			=    new_src_lang
          self.set_src_lang_special_tokens(self._src_lang)

   def __getstate__( self)      ->						Dict:







          '''simple docstring'''

          a__						:     str			=    self.__dict__.copy()
          a__						:     int			=    None
          return state

   def __setstate__( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     List[Any]			=    d

          # for backward compatibility
          if not hasattr(self ,  'sp_model_kwargs'):
                 a__						:     List[str]			=    {}

          a__						:     Union[str, Any]			=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(self.vocab_file)

   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     List[str]			=    {self.convert_ids_to_tokens(lowercase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab

   def 							__lowercase		( self ,  lowercase)      ->						List[str]:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
          a__						:     Tuple			=    self.sp_model.PieceToId(lowercase)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset)

   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Any			=    []
          a__						:     List[Any]			=    ''
          a__						:     int			=    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(lowercase) + token
                        a__						:     List[Any]			=    True
                        a__						:     int			=    []
                 else:
                        current_sub_tokens.append(lowercase)
                        a__						:     Tuple			=    False
          out_string += self.sp_model.decode(lowercase)
          return out_string.strip()

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

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

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

          return (out_vocab_file,)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = False)      ->						List[int]:







          '''simple docstring'''

          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowercase ,  token_ids_a=lowercase ,  already_has_special_tokens=lowercase)

          a__						:     Dict			=    [1] * len(self.prefix_tokens)
          a__						:     List[str]			=    [1] * len(self.suffix_tokens)
          if token_ids_a is None:
                 return prefix_ones + ([0] * len(lowercase)) + suffix_ones
          return prefix_ones + ([0] * len(lowercase)) + ([0] * len(lowercase)) + suffix_ones

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  **lowercase)      ->						List[Any]:







          '''simple docstring'''

          if src_lang is None or tgt_lang is None:
                 raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
          a__						:     str			=    src_lang
          a__						:     List[Any]			=    self(lowercase ,  add_special_tokens=lowercase ,  return_tensors=lowercase ,  **lowercase)
          a__						:     List[Any]			=    self.convert_tokens_to_ids(lowercase)
          a__						:     int			=    tgt_lang_id
          return inputs

   def 							__lowercase		( self ,  lowercase ,  lowercase = "en_XX" ,  lowercase = None ,  lowercase = "ro_RO" ,  **lowercase ,  )      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    src_lang
          a__						:     Optional[int]			=    tgt_lang
          return super().prepare_seqaseq_batch(lowercase ,  lowercase ,  **lowercase)

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







          '''simple docstring'''

          return self.set_src_lang_special_tokens(self.src_lang)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          return self.set_tgt_lang_special_tokens(self.tgt_lang)

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     str			=    self.lang_code_to_id[src_lang]
          a__						:     str			=    [self.cur_lang_code_id]
          a__						:     Optional[int]			=    [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     List[str]			=    self.lang_code_to_id[tgt_lang]
          a__						:     Optional[Any]			=    [self.cur_lang_code_id]
          a__						:     Optional[int]			=    [self.eos_token_id]









import pickle

import numpy as np
from matplotlib import pyplot as plt





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=0.2 ,  lowercase=0.2)      ->						Any:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Optional[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(lowercase ,  'wb') as f:
                 pickle.dump(lowercase ,  lowercase)

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

   @classmethod
   def 							__lowercase		( cls ,  lowercase)      ->						Any:







          '''simple docstring'''

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

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

   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          return round(lowercase ,  3)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

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

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase="average_pool")      ->						str:







          '''simple docstring'''

          a__						:     Any			=    len(featuremaps[0])
          a__						:     int			=    int(size_map / size_pooling)
          a__						:     Optional[Any]			=    []
          for i_map in range(len(lowercase)):
                 a__						:     Any			=    featuremaps[i_map]
                 a__						:     Optional[int]			=    []
                 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[str]			=    np.asmatrix(lowercase).reshape(lowercase ,  lowercase)
                 featuremap_pooled.append(lowercase)
          return featuremap_pooled

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     int			=    []
          a__						:     Optional[int]			=    0
          for i_map in range(lowercase):
                 a__						:     Optional[Any]			=    np.ones((size_map, size_map))
                 for i in range(0 ,  lowercase ,  lowercase):
                        for j in range(0 ,  lowercase ,  lowercase):
                               a__						:     Union[str, Any]			=    pd_pool[
                                   i_pool
                               ]
                               a__						:     Tuple			=    i_pool + 1
                 a__						:     Optional[int]			=    np.multiply(
                     lowercase ,  np.multiply(out_map[i_map] ,  (1 - out_map[i_map])))
                 pd_all.append(lowercase)
          return pd_all

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=bool)      ->						str:







          '''simple docstring'''

          print('----------------------Start Training-------------------------')
          print((' - - Shape: Train_Data  ', np.shape(lowercase)))
          print((' - - Shape: Teach_Data  ', np.shape(lowercase)))
          a__						:     Dict			=    0
          a__						:     List[Any]			=    []
          a__						:     Optional[int]			=    1_0000
          while rp < n_repeat and mse >= error_accuracy:
                 a__						:     Dict			=    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__						:     Any			=    np.asarray(datas_teach[p])
                        a__			,							a__						:     Optional[int]			=    self.convolute(
                            lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                        a__						:     Dict			=    self.pooling(lowercase ,  self.size_poolinga)
                        a__						:     Optional[Any]			=    np.shape(lowercase)
                        a__						:     Union[str, Any]			=    self._expand(lowercase)
                        a__						:     List[Any]			=    data_bp_input

                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)
                        a__						:     Any			=    np.dot(lowercase ,  self.wkj.T) - self.thre_bpa
                        a__						:     Any			=    self.sig(lowercase)

                        # --------------Model Leaning ------------------------
                        # calculate error and gradient---------------
                        a__						:     Any			=    np.multiply(
                            (data_teach - bp_outa) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Optional[Any]			=    np.multiply(
                            np.dot(lowercase ,  self.wkj) ,  np.multiply(lowercase ,  (1 - bp_outa)))
                        a__						:     Tuple			=    np.dot(lowercase ,  self.vji)

                        a__						:     Union[str, Any]			=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                        a__						:     List[str]			=    pd_conva_pooled.T.getA().tolist()
                        a__						:     str			=    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__						:     Optional[int]			=    self._expand_mat(pd_conva_all[k_conv])
                               a__						:     int			=    self.rate_weight * np.dot(lowercase ,  lowercase)

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

          def draw_error():
                 a__						:     int			=    [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 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     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[int]			=    self.convolute(
                     lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
                 a__						:     str			=    self.pooling(lowercase ,  self.size_poolinga)
                 a__						:     Optional[int]			=    self._expand(lowercase)

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

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     str			=    np.asmatrix(lowercase)
          a__			,							a__						:     str			=    self.convolute(
              lowercase ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
          a__						:     List[str]			=    self.pooling(lowercase ,  self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
    pass


