



'''simple docstring'''




from __future__ import annotations

lowerCamelCase							:int				    =					tuple[int, int, int]
lowerCamelCase							:Union[str, Any]				    =					tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase							:int				    =					'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCamelCase							:str				    =					'''EGZWVONAHDCLFQMSIPJBYUKXTR'''
lowerCamelCase							:Optional[int]				    =					'''FOBHMDKEXQNRAULPGSJVTYICZW'''
lowerCamelCase							:str				    =					'''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
lowerCamelCase							:int				    =					{
    '''A''': '''N''',
    '''N''': '''A''',
    '''B''': '''O''',
    '''O''': '''B''',
    '''C''': '''P''',
    '''P''': '''C''',
    '''D''': '''Q''',
    '''Q''': '''D''',
    '''E''': '''R''',
    '''R''': '''E''',
    '''F''': '''S''',
    '''S''': '''F''',
    '''G''': '''T''',
    '''T''': '''G''',
    '''H''': '''U''',
    '''U''': '''H''',
    '''I''': '''V''',
    '''V''': '''I''',
    '''J''': '''W''',
    '''W''': '''J''',
    '''K''': '''X''',
    '''X''': '''K''',
    '''L''': '''Y''',
    '''Y''': '''L''',
    '''M''': '''Z''',
    '''Z''': '''M''',
}

# -------------------------- extra rotors --------------------------
lowerCamelCase							:Any				    =					'''RMDJXFUWGISLHVTCQNKYPBEZOA'''
lowerCamelCase							:Any				    =					'''SGLCPQWZHKXAREONTFBVIYJUDM'''
lowerCamelCase							:Dict				    =					'''HVSICLTYKQUBXDWAJZOMFGPREN'''
lowerCamelCase							:Union[str, Any]				    =					'''RZWQHFMVDBKICJLNTUXAGYPSOE'''
lowerCamelCase							:int				    =					'''LFKIJODBEGAMQPXVUHYSTCZRWN'''
lowerCamelCase							:Any				    =					'''KOAEGVDHXPQZMLFTYWJNBRCIUS'''



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if (unique_rotsel := len(set(lowerCamelCase__    )    )) < 3:
						A_	:						Optional[Any]	=     f'Please use 3 unique rotors (not {unique_rotsel})'
						raise Exception(lowerCamelCase__    )

			# Checks if rotor positions are valid
			A_,    A_,    A_	:						int	=     rotpos
			if not 0 < rotorposa <= len(lowerCamelCase__    ):
						A_	:						Optional[Any]	=     f'First rotor position is not within range of 1..26 ({rotorposa}'
						raise ValueError(lowerCamelCase__    )
			if not 0 < rotorposa <= len(lowerCamelCase__    ):
						A_	:						str	=     f'Second rotor position is not within range of 1..26 ({rotorposa})'
						raise ValueError(lowerCamelCase__    )
			if not 0 < rotorposa <= len(lowerCamelCase__    ):
						A_	:						int	=     f'Third rotor position is not within range of 1..26 ({rotorposa})'
						raise ValueError(lowerCamelCase__    )

			# Validates string and returns dict
			A_	:						List[Any]	=     _plugboard(lowerCamelCase__    )

			return rotpos, rotsel, pbdict



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						List[Any]	=     f'Plugboard setting isn\'t type string ({type(lowerCamelCase__    )})'
						raise TypeError(lowerCamelCase__    )
			elif len(lowerCamelCase__    ) % 2 != 0:
						A_	:						Any	=     f'Odd number of symbols ({len(lowerCamelCase__    )})'
						raise Exception(lowerCamelCase__    )
			elif pbstring == "":
						return {}

			pbstring.replace(""" """						,      """"""    )

			# Checks if all characters are unique
			A_	:						Dict	=     set()
			for i in pbstring:
						if i not in abc:
									A_	:						Optional[int]	=     f'\'{i}\' not in list of symbols'
									raise Exception(lowerCamelCase__    )
						elif i in tmppbl:
									A_	:						Optional[Any]	=     f'Duplicate symbol ({i})'
									raise Exception(lowerCamelCase__    )
						else:
									tmppbl.add(lowerCamelCase__    )
			del tmppbl

			# Created the dictionary
			A_	:						str	=     {}
			for j in range(0						,      len(lowerCamelCase__    ) - 1						,      2    ):
						A_	:						List[Any]	=     pbstring[j + 1]
						A_	:						int	=     pbstring[j]

			return pb



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = (rotora, rotora, rotora)						,      lowerCamelCase__ = ""						,      ):
			'''simple docstring'''





			A_	:						Optional[int]	=     text.upper()
			A_,    A_,    A_	:						Any	=     _validator(
			    lowerCamelCase__						,      lowerCamelCase__						,      plugb.upper()    )

			A_,    A_,    A_	:						List[Any]	=     rotor_position
			A_,    A_,    A_	:						str	=     rotor_selection
			rotorposa -= 1
			rotorposa -= 1
			rotorposa -= 1

			A_	:						Optional[int]	=     []

			# encryption/decryption process --------------------------
			for symbol in text:
						if symbol in abc:
									# 1st plugboard --------------------------
									if symbol in plugboard:
												A_	:						Optional[int]	=     plugboard[symbol]

									# rotor ra --------------------------
									A_	:						Union[str, Any]	=     abc.index(lowerCamelCase__    ) + rotorposa
									A_	:						Union[str, Any]	=     rotora[index % len(lowerCamelCase__    )]

									# rotor rb --------------------------
									A_	:						Optional[Any]	=     abc.index(lowerCamelCase__    ) + rotorposa
									A_	:						str	=     rotora[index % len(lowerCamelCase__    )]

									# rotor rc --------------------------
									A_	:						List[Any]	=     abc.index(lowerCamelCase__    ) + rotorposa
									A_	:						Optional[Any]	=     rotora[index % len(lowerCamelCase__    )]

									# reflector --------------------------
									# this is the reason you don't need another machine to decipher

									A_	:						List[Any]	=     reflector[symbol]

									# 2nd rotors
									A_	:						Tuple	=     abc[rotora.index(lowerCamelCase__    ) - rotorposa]
									A_	:						Optional[int]	=     abc[rotora.index(lowerCamelCase__    ) - rotorposa]
									A_	:						List[str]	=     abc[rotora.index(lowerCamelCase__    ) - rotorposa]

									# 2nd plugboard
									if symbol in plugboard:
												A_	:						str	=     plugboard[symbol]

									# moves/resets rotor positions
									rotorposa += 1
									if rotorposa >= len(lowerCamelCase__    ):
												A_	:						List[Any]	=     0
												rotorposa += 1
									if rotorposa >= len(lowerCamelCase__    ):
												A_	:						Dict	=     0
												rotorposa += 1
									if rotorposa >= len(lowerCamelCase__    ):
												A_	:						Optional[int]	=     0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
						result.append(lowerCamelCase__    )

			return "".join(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCamelCase							:Optional[int]				    =					'''This is my Python script that emulates the Enigma machine from WWII.'''
						lowerCamelCase							:Tuple				    =					(1, 1, 1)
						lowerCamelCase							:str				    =					'''pictures'''
						lowerCamelCase							:Union[str, Any]				    =					(rotora, rotora, rotora)
						lowerCamelCase							:Tuple				    =					enigma(message, rotor_pos, rotor_sel, pb)

						print('''Encrypted message:''', en)
						print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))




'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	_lowerCAmelCase							:


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


	def     _a	(self       ,      lowercase							):
				# save model dict with pickle
				A_	:						Union[str, 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     _a	(cls       ,      lowercase							):
				# read saved model
				with open(lowercase       ,      """rb"""							) as f:
							A_	:						Optional[int]	=     pickle.load(lowercase							)  # noqa: S301

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


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


	def     _a	(self       ,      lowercase							):
				return round(lowercase       ,      3							)


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

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


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase="average_pool"							):
				# pooling process
				A_	:						Union[str, Any]	=     len(featuremaps[0]							)
				A_	:						str	=     int(size_map / size_pooling							)
				A_	:						List[str]	=     []
				for i_map in range(len(lowercase							)							):
							A_	:						Any	=     featuremaps[i_map]
							A_	:						Any	=     []
							for i_focus in range(0       ,      lowercase       ,      lowercase							):
										for j_focus in range(0       ,      lowercase       ,      lowercase							):
													A_	:						Tuple	=     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     _a	(self       ,      lowercase							):
				# expanding three dimension data to one dimension list
				A_	:						List[Any]	=     []
				for i in range(len(lowercase							)							):
							A_	:						Tuple	=     np.shape(data[i]							)
							A_	:						str	=     data[i].reshape(1       ,      shapes[0] * shapes[1]							)
							A_	:						Tuple	=     data_listed.getA().tolist()[0]
							data_expanded.extend(lowercase							)
				A_	:						Optional[Any]	=     np.asarray(lowercase							)
				return data_expanded


	def     _a	(self       ,      lowercase							):
				# expanding matrix to one dimension list
				A_	:						str	=     np.asarray(lowercase							)
				A_	:						Any	=     np.shape(lowercase							)
				A_	:						int	=     data_mat.reshape(1       ,      shapes[0] * shapes[1]							)
				return data_expanded


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[str]	=     []
				A_	:						Union[str, Any]	=     0
				for i_map in range(lowercase							):
							A_	:						Union[str, Any]	=     np.ones((size_map, size_map)							)
							for i in range(0       ,      lowercase       ,      lowercase							):
										for j in range(0       ,      lowercase       ,      lowercase							):
													A_	:						str	=     pd_pool[
													    i_pool
													]
													A_	:						Optional[Any]	=     i_pool + 1
							A_	:						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     _a	(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							))							)
				A_	:						Optional[Any]	=     0
				A_	:						Dict	=     []
				A_	:						List[Any]	=     10000
				while rp < n_repeat and mse >= error_accuracy:
							A_	:						List[Any]	=     0
							print(F'-------------Learning Time {rp}--------------'							)
							for p in range(len(lowercase							)							):
										# print('------------Learning Image: %d--------------'%p)
										A_	:						Optional[Any]	=     np.asmatrix(datas_train[p]							)
										A_	:						str	=     np.asarray(datas_teach[p]							)
										A_,    A_	:						Dict	=     self.convolute(
										    lowercase       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
										A_	:						Any	=     self.pooling(lowercase       ,      self.size_poolinga							)
										A_	:						int	=     np.shape(lowercase							)
										A_	:						Union[str, Any]	=     self._expand(lowercase							)
										A_	:						Dict	=     data_bp_input

										A_	:						int	=     np.dot(lowercase       ,      self.vji.T							) - self.thre_bpa
										A_	:						Any	=     self.sig(lowercase							)
										A_	:						Optional[int]	=     np.dot(lowercase       ,      self.wkj.T							) - self.thre_bpa
										A_	:						List[str]	=     self.sig(lowercase							)

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

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

													A_	:						Union[str, Any]	=     self.w_conva[k_conv] + delta_w.reshape(
													    (self.conva[0], self.conva[0])							)

													A_	:						Tuple	=     (
													    self.thre_conva[k_conv]
													    - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
													)
										# all connected layer
										A_	:						Optional[int]	=     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_	:						List[Any]	=     self.thre_bpa - pd_k_all * self.rate_thre
										A_	:						Optional[Any]	=     self.thre_bpa - pd_j_all * self.rate_thre
										# calculate the sum error of all single image
										A_	:						Optional[int]	=     np.sum(abs(data_teach - bp_outa							)							)
										error_count += errors
										# print('   ----Teach      ',data_teach)
										# print('   ----BP_output  ',bp_out3)
							A_	:						List[Any]	=     rp + 1
							A_	:						Union[str, Any]	=     error_count / patterns
							all_mse.append(lowercase							)

				def draw_error():
							A_	:						str	=     [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     _a	(self       ,      lowercase							):
				# model predict
				A_	:						Tuple	=     []
				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_	:						str	=     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_	:						Tuple	=     self._expand(lowercase							)

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


	def     _a	(self       ,      lowercase							):
				# return the data of image after convoluting process so we can check it out
				A_	:						Optional[Any]	=     np.asmatrix(lowercase							)
				A_,    A_	:						Optional[Any]	=     self.convolute(
				    lowercase       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
				A_	:						Union[str, Any]	=     self.pooling(lowercase       ,      self.size_poolinga							)

				return data_conveda, data_pooleda


if __name__ == "__main__":
						pass