






"""simple docstring"""






def       _A    (__a							)				-> bool:
    """simple docstring"""






    return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')							)



def       _A    (__a							)				-> bool:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	credit_card_number
    SCREAMING_SNAKE_CASE_   :						Tuple							=	0
    SCREAMING_SNAKE_CASE_   :						Dict							=	len(__a							) - 2
    for i in range(__a					,       -1					,       -2							):
        #  double the value of every second digit
        SCREAMING_SNAKE_CASE_   :						Tuple							=	int(cc_number[i]							)
        digit *= 2
        # If doubling of a number results in a two digit number
        # i.e greater than 9(e.g., 6 × 2 = 12),
        # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
        # to get a single digit number.
        if digit > 9:
            digit %= 10
            digit += 1
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	cc_number[:i] + str(__a							) + cc_number[i + 1 :]
        total += digit

    # Sum up the remaining digits
    for i in range(len(__a							) - 1					,       -1					,       -2							):
        total += int(cc_number[i]							)

    return total % 10 == 0



def       _A    (__a							)				-> bool:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	f'{credit_card_number} is an invalid credit card number because'
    if not credit_card_number.isdigit():
        print(f'{error_message} it has nonnumerical characters.'							)
        return False

    if not 13 <= len(__a							) <= 16:
        print(f'{error_message} of its length.'							)
        return False

    if not validate_initial_digits(__a							):
        print(f'{error_message} of its first two digits.'							)
        return False

    if not luhn_validation(__a							):
        print(f'{error_message} it fails the Luhn check.'							)
        return False

    print(f'{credit_card_number} is a valid credit card number.'							)
    return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  validate_credit_card_number("""4111111111111111""")
  validate_credit_card_number("""32323""")










"""simple docstring"""






import pickle

import numpy as np
from matplotlib import pyplot as plt




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Optional[Any]		,			lowercase_  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						List[str]		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=0.2		,			lowercase_  :						Dict=0.2):




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_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(lowercase_		,			'''wb''') as f:
              pickle.dump(lowercase_		,			lowercase_)

          print(F'Model saved： {save_path}')
      @classmethod
      def _SCREAMING_SNAKE_CASE				(		cls  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          with open(lowercase_		,			'''rb''') as f:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	pickle.load(lowercase_)  # noqa: S301

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




          '''simple docstring'''


          return 1 / (1 + np.exp(-1 * x))
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						List[str]):




          '''simple docstring'''


          return round(lowercase_		,			3)
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						Tuple		,			lowercase_  :						Dict		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[str]):




          '''simple docstring'''


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

          # expanding the data slice to One dimenssion
          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          for each_focus in data_focus:
              focusa_list.extend(self.Expand_Mat(lowercase_))
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.asarray(lowercase_)
          return focus_list, data_featuremap
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						Any="average_pool"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	len(featuremaps[0])
          SCREAMING_SNAKE_CASE_   :						Any							=	int(size_map / size_pooling)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[]
          for i_map in range(len(lowercase_)):
              SCREAMING_SNAKE_CASE_   :						int							=	featuremaps[i_map]
              SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
              for i_focus in range(0		,			lowercase_		,			lowercase_):
                  for j_focus in range(0		,			lowercase_		,			lowercase_):
                      SCREAMING_SNAKE_CASE_   :						Optional[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_))
              SCREAMING_SNAKE_CASE_   :						str							=	np.asmatrix(lowercase_).reshape(lowercase_		,			lowercase_)
              featuremap_pooled.append(lowercase_)
          return featuremap_pooled
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          for i in range(len(lowercase_)):
              SCREAMING_SNAKE_CASE_   :						List[str]							=	np.shape(data[i])
              SCREAMING_SNAKE_CASE_   :						Dict							=	data[i].reshape(1		,			shapes[0] * shapes[1])
              SCREAMING_SNAKE_CASE_   :						List[str]							=	data_listed.getA().tolist()[0]
              data_expanded.extend(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	np.asarray(lowercase_)
          return data_expanded
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.asarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.shape(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	data_mat.reshape(1		,			shapes[0] * shapes[1])
          return data_expanded
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Tuple		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						List[str]		,			lowercase_  :						Optional[int]		,			lowercase_  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	[]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	0
          for i_map in range(lowercase_):
              SCREAMING_SNAKE_CASE_   :						int							=	np.ones((size_map, size_map))
              for i in range(0		,			lowercase_		,			lowercase_):
                  for j in range(0		,			lowercase_		,			lowercase_):
                      SCREAMING_SNAKE_CASE_   :						Any							=	pd_pool[
                          i_pool
                      ]
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	i_pool + 1
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.multiply(
                  lowercase_		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])))
              pd_all.append(lowercase_)
          return pd_all
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Optional[int]		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Any		,			lowercase_  :						Optional[int]		,			lowercase_  :						Dict=bool):




          '''simple docstring'''


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

                  SCREAMING_SNAKE_CASE_   :						int							=	np.dot(lowercase_		,			self.vji.T) - self.thre_bpa
                  SCREAMING_SNAKE_CASE_   :						Dict							=	self.sig(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						str							=	np.dot(lowercase_		,			self.wkj.T) - self.thre_bpa
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	self.sig(lowercase_)

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

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

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

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

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

          print('''------------------Training Complished---------------------''')
          print((''' - - Training epoch: ''', rp, F'     - - Mse: {mse:.6f}'))
          if draw_e:
              draw_error()
          return mse
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          print('''-------------------Start Testing-------------------------''')
          print((''' - - Shape: Test_Data  ''', np.shape(lowercase_)))
          for p in range(len(lowercase_)):
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.asmatrix(datas_test[p])
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.convolute(
                  lowercase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
              SCREAMING_SNAKE_CASE_   :						Dict							=	self.pooling(lowercase_		,			self.size_poolinga)
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	self._expand(lowercase_)

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



      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.asmatrix(lowercase_)
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.convolute(
              lowercase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
          SCREAMING_SNAKE_CASE_   :						int							=	self.pooling(lowercase_		,			self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
  pass


