import pickle

import numpy as np
from matplotlib import pyplot as plt






class      __lowercase					:

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      List[str]=0.2							,							__lowerCamelCase				:      str=0.2   )							->		Tuple:


									'''simple docstring'''

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

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		{
									    '''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(__lowerCamelCase							,							'''wb'''   ) as f:
												pickle.dump(__lowerCamelCase							,							__lowerCamelCase   )

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

						@classmethod
						def        __a		(	cls				:      Optional[int]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

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

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

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									return round(__lowerCamelCase							,							3   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		convs[0]
									lowercase							=		convs[1]
									lowercase							=		np.shape(__lowerCamelCase   )[0]
									# get the data slice of original image data, data_focus
									lowercase							=		[]
									for i_focus in range(0							,							size_data - size_conv + 1							,							__lowerCamelCase   ):
												for j_focus in range(0							,							size_data - size_conv + 1							,							__lowerCamelCase   ):
															lowercase							=		data[
															    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
															]
															data_focus.append(__lowerCamelCase   )
        # calculate the feature map of every single kernel, and saved as list of matrix
									lowercase							=		[]
									lowercase							=		int((size_data - size_conv) / conv_step + 1   )
									for i_map in range(__lowerCamelCase   ):
												lowercase							=		[]
												for i_focus in range(len(__lowerCamelCase   )   ):
															lowercase							=		(
															    np.sum(np.multiply(data_focus[i_focus]							,							w_convs[i_map]   )   )
															    - thre_convs[i_map]
															)
															featuremap.append(self.sig(__lowerCamelCase   )   )
												lowercase							=		np.asmatrix(__lowerCamelCase   ).reshape(
												    __lowerCamelCase							,							__lowerCamelCase   )
												data_featuremap.append(__lowerCamelCase   )

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

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]="average_pool"   )							->		str:


									'''simple docstring'''

									lowercase							=		len(featuremaps[0]   )
									lowercase							=		int(size_map / size_pooling   )
									lowercase							=		[]
									for i_map in range(len(__lowerCamelCase   )   ):
												lowercase							=		featuremaps[i_map]
												lowercase							=		[]
												for i_focus in range(0							,							__lowerCamelCase							,							__lowerCamelCase   ):
															for j_focus in range(0							,							__lowerCamelCase							,							__lowerCamelCase   ):
																		lowercase							=		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(__lowerCamelCase   )   )
																		elif pooling_type == "max_pooling":
																					# max pooling
																					map_pooled.append(np.max(__lowerCamelCase   )   )
												lowercase							=		np.asmatrix(__lowerCamelCase   ).reshape(__lowerCamelCase							,							__lowerCamelCase   )
												featuremap_pooled.append(__lowerCamelCase   )
									return featuremap_pooled

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		[]
									for i in range(len(__lowerCamelCase   )   ):
												lowercase							=		np.shape(data[i]   )
												lowercase							=		data[i].reshape(1							,							shapes[0] * shapes[1]   )
												lowercase							=		data_listed.getA().tolist()[0]
												data_expanded.extend(__lowerCamelCase   )
									lowercase							=		np.asarray(__lowerCamelCase   )
									return data_expanded

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		np.asarray(__lowerCamelCase   )
									lowercase							=		np.shape(__lowerCamelCase   )
									lowercase							=		data_mat.reshape(1							,							shapes[0] * shapes[1]   )
									return data_expanded

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[Any]   )							->		Dict:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[Any]=bool   )							->		str:


									'''simple docstring'''

									print('''----------------------Start Training-------------------------'''   )
									print((''' - - Shape: Train_Data  ''', np.shape(__lowerCamelCase   ))   )
									print((''' - - Shape: Teach_Data  ''', np.shape(__lowerCamelCase   ))   )
									lowercase							=		0
									lowercase							=		[]
									lowercase							=		1_00_00
									while rp < n_repeat and mse >= error_accuracy:
												lowercase							=		0
												print(f'-------------Learning Time {rp}--------------'   )
												for p in range(len(__lowerCamelCase   )   ):
															# print('------------Learning Image: %d--------------'%p)
															lowercase							=		np.asmatrix(datas_train[p]   )
															lowercase							=		np.asarray(datas_teach[p]   )
															lowercase		,lowercase							=		self.convolute(
															    __lowerCamelCase							,							self.conva							,							self.w_conva							,							self.thre_conva							,							conv_step=self.step_conva							,							)
															lowercase							=		self.pooling(__lowerCamelCase							,							self.size_poolinga   )
															lowercase							=		np.shape(__lowerCamelCase   )
															lowercase							=		self._expand(__lowerCamelCase   )
															lowercase							=		data_bp_input

															lowercase							=		np.dot(__lowerCamelCase							,							self.vji.T   ) - self.thre_bpa
															lowercase							=		self.sig(__lowerCamelCase   )
															lowercase							=		np.dot(__lowerCamelCase							,							self.wkj.T   ) - self.thre_bpa
															lowercase							=		self.sig(__lowerCamelCase   )

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

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

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

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

									def draw_error():
												lowercase							=		[error_accuracy for i in range(int(n_repeat * 1.2   )   )]
												plt.plot(__lowerCamelCase							,							'''+-'''   )
												plt.plot(__lowerCamelCase							,							'''r--'''   )
												plt.xlabel('''Learning Times'''   )
												plt.ylabel('''All_mse'''   )
												plt.grid(__lowerCamelCase							,							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				:      str							,							__lowerCamelCase				:      Any   )							->		Any:


									'''simple docstring'''

									lowercase							=		[]
									print('''-------------------Start Testing-------------------------'''   )
									print((''' - - Shape: Test_Data  ''', np.shape(__lowerCamelCase   ))   )
									for p in range(len(__lowerCamelCase   )   ):
												lowercase							=		np.asmatrix(datas_test[p]   )
												lowercase		,lowercase							=		self.convolute(
												    __lowerCamelCase							,							self.conva							,							self.w_conva							,							self.thre_conva							,							conv_step=self.step_conva							,							)
												lowercase							=		self.pooling(__lowerCamelCase							,							self.size_poolinga   )
												lowercase							=		self._expand(__lowerCamelCase   )

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



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		np.asmatrix(__lowerCamelCase   )
									lowercase		,lowercase							=		self.convolute(
									    __lowerCamelCase							,							self.conva							,							self.w_conva							,							self.thre_conva							,							conv_step=self.step_conva							,							)
									lowercase							=		self.pooling(__lowerCamelCase							,							self.size_poolinga   )

									return data_conveda, data_pooleda


if __name__ == "__main__":
						pass






from __future__ import annotations

import math






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:



			"""simple docstring"""







			if num <= 0:
						lowercase							=		f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(UpperCAmelCase       )

			lowercase							=		[True] * (num + 1)
			lowercase							=		[]
			lowercase							=		2
			lowercase							=		int(math.sqrt(UpperCAmelCase       )       )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(UpperCAmelCase       )

									# Set multiples of start be False
									for i in range(start * start,		num + 1,		UpperCAmelCase       ):
												if sieve[i] is True:
															lowercase							=		False

						start += 1

			for j in range(end + 1,		num + 1       ):
						if sieve[j] is True:
									prime.append(UpperCAmelCase       )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))





