




import pickle

import numpy as np
from matplotlib import pyplot as plt




class       a__	:







					def __init__(     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A=0.2  ,		A=0.2			)  ->   Any:



										'''simple docstring'''



										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       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



										'''simple docstring'''



										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(A  ,		"wb"			) as f:
															pickle.dump(A  ,		A			)

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







					@classmethod
					def       lowerCAmelCase_    (     cls  ,		A			)  ->   Optional[Any]:



										'''simple docstring'''



										with open(A  ,		"rb"			) as f:
															a     =				pickle.load(A			)  # 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(A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)
										# 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       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



										'''simple docstring'''



										return round(A  ,		3			)







					def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   str:



										'''simple docstring'''



										a     =				convs[0]
										a     =				convs[1]
										a     =				np.shape(A			)[0]
										# get the data slice of original image data, data_focus
										a     =				[]
										for i_focus in range(0  ,		size_data - size_conv + 1  ,		A			):
															for j_focus in range(0  ,		size_data - size_conv + 1  ,		A			):
																				a     =				data[
																				    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																				]
																				data_focus.append(A			)
        # 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(A			):
															a     =				[]
															for i_focus in range(len(A			)			):
																				a     =				(
																				    np.sum(np.multiply(data_focus[i_focus]  ,		w_convs[i_map]			)			)
																				    - thre_convs[i_map]
																				)
																				featuremap.append(self.sig(A			)			)
															a     =				np.asmatrix(A			).reshape(
															    A  ,		A			)
															data_featuremap.append(A			)

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







					def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A="average_pool"			)  ->   Tuple:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self  ,		A			)  ->   List[Any]:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Tuple:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A=bool			)  ->   str:



										'''simple docstring'''



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

																				a     =				np.dot(A  ,		self.vji.T			) - self.thre_bpa
																				a     =				self.sig(A			)
																				a     =				np.dot(A  ,		self.wkj.T			) - self.thre_bpa
																				a     =				self.sig(A			)

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

																									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(A			)

										def draw_error():
															a     =				[error_accuracy for i in range(int(n_repeat * 1.2			)			)]
															plt.plot(A  ,		"+-"			)
															plt.plot(A  ,		"r--"			)
															plt.xlabel("Learning Times"			)
															plt.ylabel("All_mse"			)
															plt.grid(A  ,		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  ,		A			)  ->   int:



										'''simple docstring'''



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

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







					def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



										'''simple docstring'''



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

										return data_conveda, data_pooleda


if __name__ == "__main__":
			pass







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

