



import pickle

import numpy as np
from matplotlib import pyplot as plt





class      lowercase__    :







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # save model dict with pickle
       lowerCAmelCase_					:       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 	UpperCAmelCase__		(	cls    ,						_lowercase						):
       # read saved model
       with open(_lowercase    ,						"""rb"""						) as f:
            lowerCAmelCase_					:       Optional[Any]    =		pickle.load(_lowercase						)  # noqa: S301

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







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return round(_lowercase    ,						3						)







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

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







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase="average_pool"						):
       # pooling process
       lowerCAmelCase_					:       List[Any]    =		len(featuremaps[0]						)
       lowerCAmelCase_					:       str    =		int(size_map / size_pooling						)
       lowerCAmelCase_					:       Dict    =		[]
       for i_map in range(len(_lowercase						)						):
            lowerCAmelCase_					:       Optional[Any]    =		featuremaps[i_map]
            lowerCAmelCase_					:       Optional[int]    =		[]
            for i_focus in range(0    ,						_lowercase    ,						_lowercase						):
                 for j_focus in range(0    ,						_lowercase    ,						_lowercase						):
                      lowerCAmelCase_					:       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						)						)
            lowerCAmelCase_					:       str    =		np.asmatrix(_lowercase						).reshape(_lowercase    ,						_lowercase						)
            featuremap_pooled.append(_lowercase						)
       return featuremap_pooled







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # expanding three dimension data to one dimension list
       lowerCAmelCase_					:       List[Any]    =		[]
       for i in range(len(_lowercase						)						):
            lowerCAmelCase_					:       Any    =		np.shape(data[i]						)
            lowerCAmelCase_					:       List[Any]    =		data[i].reshape(1    ,						shapes[0] * shapes[1]						)
            lowerCAmelCase_					:       int    =		data_listed.getA().tolist()[0]
            data_expanded.extend(_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		np.asarray(_lowercase						)
       return data_expanded







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # expanding matrix to one dimension list
       lowerCAmelCase_					:       Dict    =		np.asarray(_lowercase						)
       lowerCAmelCase_					:       Tuple    =		np.shape(_lowercase						)
       lowerCAmelCase_					:       int    =		data_mat.reshape(1    ,						shapes[0] * shapes[1]						)
       return data_expanded







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







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

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

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

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

                      lowerCAmelCase_					:       Optional[int]    =		self.w_conva[k_conv] + delta_w.reshape(
                          (self.conva[0], self.conva[0])						)

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

       def draw_error():
            lowerCAmelCase_					:       Optional[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 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # model predict
       lowerCAmelCase_					:       List[str]    =		[]
       print("""-------------------Start Testing-------------------------"""						)
       print((""" - - Shape: Test_Data  """, np.shape(_lowercase						))						)
       for p in range(len(_lowercase						)						):
            lowerCAmelCase_					:       Union[str, Any]    =		np.asmatrix(datas_test[p]						)
            lowerCAmelCase_					:       Any    =		self.convolute(
                _lowercase    ,						self.conva    ,						self.w_conva    ,						self.thre_conva    ,						conv_step=self.step_conva    ,						)
            lowerCAmelCase_					:       str    =		self.pooling(_lowercase    ,						self.size_poolinga						)
            lowerCAmelCase_					:       str    =		self._expand(_lowercase						)

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







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       # return the data of image after convoluting process so we can check it out
       lowerCAmelCase_					:       Any    =		np.asmatrix(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		self.convolute(
           _lowercase    ,						self.conva    ,						self.w_conva    ,						self.thre_conva    ,						conv_step=self.step_conva    ,						)
       lowerCAmelCase_					:       int    =		self.pooling(_lowercase    ,						self.size_poolinga						)

       return data_conveda, data_pooleda


if __name__ == "__main__":
      pass







from collections.abc import Generator
from math import sin






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     if len(_a			) != 32:
          raise ValueError("""Input must be of length 32"""			)

     lowerCAmelCase_					:       Any    =		B""""""
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 							_lowerCAmelCase (  _a   :					int			)	->			bytes:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       Tuple    =		format(_a					,   """08x"""			)[-8:]
     lowerCAmelCase_					:       Any    =		B""""""
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""			)
     return little_endian_hex






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Tuple    =		B""""""
     for char in message:
          bit_string += format(_a					,   """08b"""			).encode("""utf-8"""			)
     lowerCAmelCase_					:       Dict    =		format(len(_a			)					,   """064b"""			).encode("""utf-8"""			)

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_a			) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]			) + to_little_endian(start_len[:32]			)

     return bit_string






def 							_lowerCAmelCase (  _a   :					bytes			)	->			Generator[list[int], None, None]:
     if len(_a			) % 5_12 != 0:
          raise ValueError("""Input must have length that's a multiple of 512"""			)

     for pos in range(0					,   len(_a			)					,   5_12			):
          lowerCAmelCase_					:       int    =		bit_string[pos : pos + 5_12]
          lowerCAmelCase_					:       Any    =		[]
          for i in range(0					,   5_12					,   32			):
               block_words.append(int(to_little_endian(block[i : i + 32]			)					,   2			)			)
          yield block_words






def 							_lowerCAmelCase (  _a   :					int			)	->			int:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       List[str]    =		format(_a					,   """032b"""			)
     lowerCAmelCase_					:       Optional[int]    =		""""""
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_a					,   2			)






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     return (a + b) % 2**32






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)
     if shift < 0:
          raise ValueError("""Shift must be non-negative"""			)
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Union[str, Any]    =		preprocess(_a			)

     lowerCAmelCase_					:       Optional[int]    =		[int(2**32 * abs(sin(i + 1			)			)			) for i in range(64			)]

     # Starting states
     lowerCAmelCase_					:       Tuple    =		0X67452301
     lowerCAmelCase_					:       Optional[int]    =		0XEFCDAB89
     lowerCAmelCase_					:       Tuple    =		0X98BADCFE
     lowerCAmelCase_					:       Tuple    =		0X10325476

     lowerCAmelCase_					:       Dict    =		[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_a			):
          lowerCAmelCase_					:       int    =		aa
          lowerCAmelCase_					:       Any    =		ba
          lowerCAmelCase_					:       List[str]    =		ca
          lowerCAmelCase_					:       Optional[Any]    =		da

          # Hash current chunk
          for i in range(64			):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    lowerCAmelCase_					:       int    =		d ^ (b & (c ^ d))
                    lowerCAmelCase_					:       List[Any]    =		i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    lowerCAmelCase_					:       List[Any]    =		c ^ (d & (b ^ c))
                    lowerCAmelCase_					:       Optional[Any]    =		(5 * i + 1) % 16
               elif i <= 47:
                    lowerCAmelCase_					:       Union[str, Any]    =		b ^ c ^ d
                    lowerCAmelCase_					:       Optional[Any]    =		(3 * i + 5) % 16
               else:
                    lowerCAmelCase_					:       Any    =		c ^ (b | not_aa(_a			))
                    lowerCAmelCase_					:       List[str]    =		(7 * i) % 16
               lowerCAmelCase_					:       Tuple    =		(f + a + added_consts[i] + block_words[g]) % 2**32
               lowerCAmelCase_					:       Tuple    =		d
               lowerCAmelCase_					:       Optional[Any]    =		c
               lowerCAmelCase_					:       Dict    =		b
               lowerCAmelCase_					:       List[Any]    =		sum_aa(_a					,   left_rotate_aa(_a					,   shift_amounts[i]			)			)

          # Add hashed chunk to running total
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Dict    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Tuple    =		sum_aa(_a					,   _a			)

     lowerCAmelCase_					:       int    =		reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			)
     return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()
