




'''simple docstring'''
import pickle

import numpy as np
from matplotlib import pyplot as plt
class   __UpperCAmelCase				:



					'''simple docstring'''





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



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Optional[int])    ->							str:
								# save model dict with pickle
								A_									= {
								    '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     __snake_case						(	cls     :						Optional[int]      ,       _lowercase     :						Union[str, Any])    ->							Optional[int]:
								# read saved model
								with open(_lowercase      ,       'rb') as f:
											A_									= pickle.load(_lowercase)  # noqa: S301

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



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						Union[str, Any])    ->							List[Any]:
								return 1 / (1 + np.exp(-1 * x))



					def     __snake_case						(	self     :						int      ,       _lowercase     :						Optional[int])    ->							Union[str, Any]:
								return round(_lowercase      ,       3)



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

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



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Tuple      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Union[str, Any]="average_pool")    ->							Optional[Any]:
								# pooling process
								A_									= len(featuremaps[0])
								A_									= int(size_map / size_pooling)
								A_									= []
								for i_map in range(len(_lowercase)):
											A_									= featuremaps[i_map]
											A_									= []
											for i_focus in range(0      ,       _lowercase      ,       _lowercase):
														for j_focus in range(0      ,       _lowercase      ,       _lowercase):
																	A_									= 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_									= np.asmatrix(_lowercase).reshape(_lowercase      ,       _lowercase)
											featuremap_pooled.append(_lowercase)
								return featuremap_pooled



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



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



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



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

														A_									= np.dot(_lowercase      ,       self.vji.T) - self.thre_bpa
														A_									= self.sig(_lowercase)
														A_									= np.dot(_lowercase      ,       self.wkj.T) - self.thre_bpa
														A_									= self.sig(_lowercase)

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

														A_									= pd_i_all / (self.size_poolinga * self.size_poolinga)
														A_									= pd_conva_pooled.T.getA().tolist()
														A_									= 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_									= self._expand_mat(pd_conva_all[k_conv])
																	A_									= self.rate_weight * np.dot(_lowercase      ,       _lowercase)

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

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

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

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



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

								return data_conveda, data_pooleda


if __name__ == "__main__":
							pass










'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

			# since string of zero length match pattern of zero length
			A_									= 1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= dp[0][j - 2] if pattern[j - 1] == '*' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															A_									= 1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															A_									= dp[i - 1][j]
												else:
															A_									= 0
									else:
												A_									= 0

			return bool(dp[-1][-1]     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							_SCREAMING_SNAKE_CASE			=						"aab"
							_SCREAMING_SNAKE_CASE			=						"c*a*b"

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(f"""{input_string} matches the given pattern {pattern}""")
							else:
														print(f"""{input_string} does not match with the given pattern {pattern}""")




