

import pickle

import numpy as np
from matplotlib import pyplot as plt


class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=0.2			,					__UpperCamelCase=0.2				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			{
				    '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(__UpperCamelCase			,					'wb'				) as f:
					pickle.dump(__UpperCamelCase			,					__UpperCamelCase				)

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


			@classmethod
			def       __lowerCAmelCase			(     cls			,					__UpperCamelCase				):

				"""simple docstring"""


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

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return round(__UpperCamelCase			,					3				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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

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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase="average_pool"				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=bool				):

				"""simple docstring"""


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

						snake_case_   		=			np.dot(__UpperCamelCase			,					self.vji.T				) - self.thre_bpa
						snake_case_   		=			self.sig(__UpperCamelCase				)
						snake_case_   		=			np.dot(__UpperCamelCase			,					self.wkj.T				) - self.thre_bpa
						snake_case_   		=			self.sig(__UpperCamelCase				)

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

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

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

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

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

				print('------------------Training Complished---------------------'				)
				print((' - - Training epoch: ', rp, f"""     - - Mse: {mse:.6f}""")				)
				if draw_e:
					draw_error()
				return mse


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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

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







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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

				return data_conveda, data_pooleda


if __name__ == "__main__":
		pass



def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			len(lowercase__						)
	for _ in range(lowercase__						):
		for i in range(_ % 2      ,			arr_size - 1      ,			2						):
			if arr[i + 1] < arr[i]:
				snake_case_		,							snake_case_   		=			arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
		A									=  list(range(10, 0, -1))
		print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
