

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCamelCase__  :      List[str]			=    logging.get_logger(__name__)


UpperCamelCase__  :      List[str]			=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__  :      Tuple			=    {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__  :      Any			=    {
    'allenai/led-base-16384': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   _UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						(
        list(range(ord('!'       ) ,   ord('~'       ) + 1       )       ) + list(range(ord('¡'       ) ,   ord('¬'       ) + 1       )       ) + list(range(ord('®'       ) ,   ord('ÿ'       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE_									=						bs[:]
    SCREAMING_SNAKE_CASE_									=						0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(lowerCAmelCase_       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE_									=						[chr(lowerCAmelCase_       ) for n in cs]
    return dict(zip(lowerCAmelCase_ ,   lowerCAmelCase_       )       )


def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						set()
    SCREAMING_SNAKE_CASE_									=						word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE_									=						char
    return pairs





class       __snake_case  (    a__       ):
  __lowerCAmelCase     :	Optional[Any]						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	int						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	int						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	Optional[int]						 =  ["""input_ids""", """attention_mask"""]

  def __init__(					self						,		_A						,		_A						,		_A="replace"						,		_A="<s>"						,		_A="</s>"						,		_A="</s>"						,		_A="<s>"						,		_A="<unk>"						,		_A="<pad>"						,		_A="<mask>"						,		_A=False						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else bos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else eos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else sep_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else cls_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else unk_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowercase__						,		lstrip=lowercase__						,		rstrip=lowercase__) if isinstance(lowercase__						,		lowercase__) else mask_token

      super().__init__(
          errors=lowercase__						,		bos_token=lowercase__						,		eos_token=lowercase__						,		unk_token=lowercase__						,		sep_token=lowercase__						,		cls_token=lowercase__						,		pad_token=lowercase__						,		mask_token=lowercase__						,		add_prefix_space=lowercase__						,		**lowercase__						,		)

      with open(lowercase__						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(lowercase__)
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.encoder.items()}
      SCREAMING_SNAKE_CASE_									=						errors  # how to handle errors in decoding
      SCREAMING_SNAKE_CASE_									=						bytes_to_unicode()
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.byte_encoder.items()}
      with open(lowercase__						,		encoding='utf-8') as merges_handle:
          SCREAMING_SNAKE_CASE_									=						merges_handle.read().split('\n')[1:-1]
      SCREAMING_SNAKE_CASE_									=						[tuple(merge.split()) for merge in bpe_merges]
      SCREAMING_SNAKE_CASE_									=						dict(zip(lowercase__						,		range(len(lowercase__))))
      SCREAMING_SNAKE_CASE_									=						{}
      SCREAMING_SNAKE_CASE_									=						add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      SCREAMING_SNAKE_CASE_									=						re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')

  @property
  # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
  def 						lowerCAmelCase__     (					self):
      return len(self.encoder)

  def 						lowerCAmelCase__     (					self):
      return dict(self.encoder						,		**self.added_tokens_encoder)

  def 						lowerCAmelCase__     (					self						,		_A):





      if token in self.cache:
          return self.cache[token]
      SCREAMING_SNAKE_CASE_									=						tuple(lowercase__)
      SCREAMING_SNAKE_CASE_									=						get_pairs(lowercase__)

      if not pairs:
          return token

      while True:
          SCREAMING_SNAKE_CASE_									=						min(lowercase__						,		key=lambda _A: self.bpe_ranks.get(lowercase__						,		float('inf')))
          if bigram not in self.bpe_ranks:
              break
          SCREAMING_SNAKE_CASE_									=						bigram
          SCREAMING_SNAKE_CASE_									=						[]
          SCREAMING_SNAKE_CASE_									=						0
          while i < len(lowercase__):
              try:
                  SCREAMING_SNAKE_CASE_									=						word.index(lowercase__						,		lowercase__)
              except ValueError:
                  new_word.extend(word[i:])
                  break
              else:
                  new_word.extend(word[i:j])
                  SCREAMING_SNAKE_CASE_									=						j

              if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
                  new_word.append(first + second)
                  i += 2
              else:
                  new_word.append(word[i])
                  i += 1
          SCREAMING_SNAKE_CASE_									=						tuple(lowercase__)
          SCREAMING_SNAKE_CASE_									=						new_word
          if len(lowercase__) == 1:
              break
          else:
              SCREAMING_SNAKE_CASE_									=						get_pairs(lowercase__)
      SCREAMING_SNAKE_CASE_									=						''' '''.join(lowercase__)
      SCREAMING_SNAKE_CASE_									=						word
      return word

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      for token in re.findall(self.pat						,		lowercase__):
          SCREAMING_SNAKE_CASE_									=						''''''.join(
              self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__).split(' '))
      return bpe_tokens

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.encoder.get(lowercase__						,		self.encoder.get(self.unk_token))

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.decoder.get(lowercase__)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''''''.join(lowercase__)
      SCREAMING_SNAKE_CASE_									=						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'						,		errors=self.errors)
      return text

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):





      if not os.path.isdir(lowercase__):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          lowercase__						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          lowercase__						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

      with open(lowercase__						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.encoder						,		indent=2						,		sort_keys=lowercase__						,		ensure_ascii=lowercase__) + '\n')

      SCREAMING_SNAKE_CASE_									=						0
      with open(lowercase__						,		'w'						,		encoding='utf-8') as writer:
          writer.write('#version: 0.2\n')
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,		key=lambda _A: kv[1]):
              if index != token_index:
                  logger.warning(
                      f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!')
                  SCREAMING_SNAKE_CASE_									=						token_index
              writer.write(' '.join(lowercase__) + '\n')
              index += 1

      return vocab_file, merge_file

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):





      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):





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

      if token_ids_a is None:
          return [1] + ([0] * len(lowercase__)) + [1]
      return [1] + ([0] * len(lowercase__)) + [1, 1] + ([0] * len(lowercase__)) + [1]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=False						,		**_A):
      SCREAMING_SNAKE_CASE_									=						kwargs.pop('add_prefix_space'						,		self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(lowercase__) > 0 and not text[0].isspace()):
          SCREAMING_SNAKE_CASE_									=						''' ''' + text
      return (text, kwargs)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = PaddingStrategy.DO_NOT_PAD						,		_A = None						,		_A = None						,		):
      SCREAMING_SNAKE_CASE_									=						super()._pad(
          encoded_inputs=lowercase__						,		max_length=lowercase__						,		padding_strategy=lowercase__						,		pad_to_multiple_of=lowercase__						,		return_attention_mask=lowercase__						,		)

      # Load from model defaults
      if return_attention_mask is None:
          SCREAMING_SNAKE_CASE_									=						'''attention_mask''' in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
          SCREAMING_SNAKE_CASE_									=						encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          SCREAMING_SNAKE_CASE_									=						len(encoded_inputs['global_attention_mask']) != len(lowercase__)

          if needs_to_be_padded:
              SCREAMING_SNAKE_CASE_									=						len(lowercase__) - len(encoded_inputs['global_attention_mask'])

              if self.padding_side == "right":
                  # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                  SCREAMING_SNAKE_CASE_									=						(
                      encoded_inputs['''global_attention_mask'''] + [-1] * difference
                  )
              elif self.padding_side == "left":
                  SCREAMING_SNAKE_CASE_									=						[-1] * difference + encoded_inputs[
                      '''global_attention_mask'''
                  ]
              else:
                  raise ValueError('Invalid padding strategy:' + str(self.padding_side))

      return encoded_inputs





import pickle

import numpy as np
from matplotlib import pyplot as plt



class       __snake_case  :

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=0.2						,		_A=0.2):
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						conva_get[:2]
      SCREAMING_SNAKE_CASE_									=						conva_get[2]
      SCREAMING_SNAKE_CASE_									=						size_pa
      SCREAMING_SNAKE_CASE_									=						rate_w
      SCREAMING_SNAKE_CASE_									=						rate_t
      SCREAMING_SNAKE_CASE_									=						[
          np.mat(-1 * np.random.rand(self.conva[0]						,		self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.conva[1]) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1

  def 						lowerCAmelCase__     (					self						,		_A):
      # save model dict with pickle
      SCREAMING_SNAKE_CASE_									=						{
          'num_bp1': self.num_bpa,
          'num_bp2': self.num_bpa,
          'num_bp3': self.num_bpa,
          'conv1': self.conva,
          'step_conv1': self.step_conva,
          'size_pooling1': self.size_poolinga,
          'rate_weight': self.rate_weight,
          'rate_thre': self.rate_thre,
          'w_conv1': self.w_conva,
          'wkj': self.wkj,
          'vji': self.vji,
          'thre_conv1': self.thre_conva,
          'thre_bp2': self.thre_bpa,
          'thre_bp3': self.thre_bpa,
      }
      with open(_A						,		'wb') as f:
          pickle.dump(_A						,		_A)

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

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A):
      # read saved model
      with open(_A						,		'rb') as f:
          SCREAMING_SNAKE_CASE_									=						pickle.load(_A)  # noqa: S301

      SCREAMING_SNAKE_CASE_									=						model_dic.get('conv1')
      conv_get.append(model_dic.get('step_conv1'))
      SCREAMING_SNAKE_CASE_									=						model_dic.get('size_pooling1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp3')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_weight')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_thre')
      # create model instance
      SCREAMING_SNAKE_CASE_									=						CNN(_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A)
      # modify model parameter
      SCREAMING_SNAKE_CASE_									=						model_dic.get('w_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('wkj')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('vji')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp3')
      return conv_ins

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

  def 						lowerCAmelCase__     (					self						,		_A):
      return round(_A						,		3)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      # convolution process
      SCREAMING_SNAKE_CASE_									=						convs[0]
      SCREAMING_SNAKE_CASE_									=						convs[1]
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)[0]
      # get the data slice of original image data, data_focus
      SCREAMING_SNAKE_CASE_									=						[]
      for i_focus in range(0						,		size_data - size_conv + 1						,		_A):
          for j_focus in range(0						,		size_data - size_conv + 1						,		_A):
              SCREAMING_SNAKE_CASE_									=						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
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						int((size_data - size_conv) / conv_step + 1)
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(len(_A)):
              SCREAMING_SNAKE_CASE_									=						(
                  np.sum(np.multiply(data_focus[i_focus]						,		w_convs[i_map]))
                  - thre_convs[i_map]
              )
              featuremap.append(self.sig(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(
              _A						,		_A)
          data_featuremap.append(_A)

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A="average_pool"):
      # pooling process
      SCREAMING_SNAKE_CASE_									=						len(featuremaps[0])
      SCREAMING_SNAKE_CASE_									=						int(size_map / size_pooling)
      SCREAMING_SNAKE_CASE_									=						[]
      for i_map in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						featuremaps[i_map]
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(0						,		_A						,		_A):
              for j_focus in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						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))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(_A						,		_A)
          featuremap_pooled.append(_A)
      return featuremap_pooled

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding three dimension data to one dimension list
      SCREAMING_SNAKE_CASE_									=						[]
      for i in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.shape(data[i])
          SCREAMING_SNAKE_CASE_									=						data[i].reshape(1						,		shapes[0] * shapes[1])
          SCREAMING_SNAKE_CASE_									=						data_listed.getA().tolist()[0]
          data_expanded.extend(_A)
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding matrix to one dimension list
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)
      SCREAMING_SNAKE_CASE_									=						data_mat.reshape(1						,		shapes[0] * shapes[1])
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						0
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						np.ones((size_map, size_map))
          for i in range(0						,		_A						,		_A):
              for j in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						pd_pool[
                      i_pool
                  ]
                  SCREAMING_SNAKE_CASE_									=						i_pool + 1
          SCREAMING_SNAKE_CASE_									=						np.multiply(
              _A						,		np.multiply(out_map[i_map]						,		(1 - out_map[i_map])))
          pd_all.append(_A)
      return pd_all

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=bool):
      # model traning
      print('----------------------Start Training-------------------------')
      print((' - - Shape: Train_Data  ', np.shape(_A)))
      print((' - - Shape: Teach_Data  ', np.shape(_A)))
      SCREAMING_SNAKE_CASE_									=						0
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						10000
      while rp < n_repeat and mse >= error_accuracy:
          SCREAMING_SNAKE_CASE_									=						0
          print(f"""-------------Learning Time {rp}--------------""")
          for p in range(len(_A)):
              # print('------------Learning Image: %d--------------'%p)
              SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_train[p])
              SCREAMING_SNAKE_CASE_									=						np.asarray(datas_teach[p])
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
                  _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
              SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						np.shape(_A)
              SCREAMING_SNAKE_CASE_									=						self._expand(_A)
              SCREAMING_SNAKE_CASE_									=						data_bp_input

              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.wkj.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)

              # --------------Model Leaning ------------------------
              # calculate error and gradient---------------
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  (data_teach - bp_outa)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  np.dot(_A						,		self.wkj)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji)

              SCREAMING_SNAKE_CASE_									=						pd_i_all / (self.size_poolinga * self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						pd_conva_pooled.T.getA().tolist()
              SCREAMING_SNAKE_CASE_									=						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]):
                  SCREAMING_SNAKE_CASE_									=						self._expand_mat(pd_conva_all[k_conv])
                  SCREAMING_SNAKE_CASE_									=						self.rate_weight * np.dot(_A						,		_A)

                  SCREAMING_SNAKE_CASE_									=						self.w_conva[k_conv] + delta_w.reshape(
                      (self.conva[0], self.conva[0]))

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

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

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

  def 						lowerCAmelCase__     (					self						,		_A):
      # model predict
      SCREAMING_SNAKE_CASE_									=						[]
      print('-------------------Start Testing-------------------------')
      print((' - - Shape: Test_Data  ', np.shape(_A)))
      for p in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_test[p])
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
              _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
          SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
          SCREAMING_SNAKE_CASE_									=						self._expand(_A)

          SCREAMING_SNAKE_CASE_									=						data_bp_input
          SCREAMING_SNAKE_CASE_									=						bp_outa * self.vji.T - self.thre_bpa
          SCREAMING_SNAKE_CASE_									=						self.sig(_A)
          SCREAMING_SNAKE_CASE_									=						bp_outa * self.wkj.T - self.thre_bpa
          SCREAMING_SNAKE_CASE_									=						self.sig(_A)
          produce_out.extend(bp_outa.getA().tolist())
      SCREAMING_SNAKE_CASE_									=						[list(map(self.do_round						,		_A)) for each in produce_out]
      return np.asarray(_A)


  def 						lowerCAmelCase__     (					self						,		_A):
      # return the data of image after convoluting process so we can check it out
      SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
          _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
      SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


