



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_								=     logging.get_logger(__name__)
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Any     		="""timm_backbone"""




				def __init__(						self					:						Any,					__lowercase					:						str=None,					__lowercase					:						str=3,					__lowercase					:						str=True,					__lowercase					:						Union[str, Any]=True,					__lowercase					:						Optional[int]=None,					**__lowercase					:						int,					):
							super().__init__(**__lowercase						)
							lowercase__					  =				backbone
							lowercase__					  =				num_channels
							lowercase__					  =				features_only
							lowercase__					  =				use_pretrained_backbone
							lowercase__					  =				True
							lowercase__					  =				out_indices if out_indices is not None else (-1,)









import pickle

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




				def __init__(						self					:						Tuple,					__lowercase					:						Union[str, Any],					__lowercase					:						int,					__lowercase					:						Union[str, Any],					__lowercase					:						str,					__lowercase					:						List[Any],					__lowercase					:						List[str]=0.2,					__lowercase					:						List[str]=0.2						):
							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					:						Any,					__lowercase					:						List[str]						):
							# save model dict with pickle
							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(__lowercase,					"wb"						) as f:
										pickle.dump(__lowercase,					__lowercase						)

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




				@classmethod
				def 							A__      (						cls					:						Dict,					__lowercase					:						Union[str, Any]						):
							# read saved model
							with open(__lowercase,					"rb"						) as f:
										lowercase__					  =				pickle.load(__lowercase						)  # 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(__lowercase,					__lowercase,					__lowercase,					__lowercase,					__lowercase,					__lowercase,					__lowercase						)
							# 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,					__lowercase					:						List[Any]						):
							return 1 / (1 + np.exp(-1 * x						))




				def 							A__      (						self					:						List[str],					__lowercase					:						Optional[Any]						):
							return round(__lowercase,					3						)




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

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




				def 							A__      (						self					:						List[Any],					__lowercase					:						Any,					__lowercase					:						List[Any],					__lowercase					:						Union[str, Any]="average_pool"						):
							# pooling process
							lowercase__					  =				len(featuremaps[0]						)
							lowercase__					  =				int(size_map / size_pooling						)
							lowercase__					  =				[]
							for i_map in range(len(__lowercase						)						):
										lowercase__					  =				featuremaps[i_map]
										lowercase__					  =				[]
										for i_focus in range(0,					__lowercase,					__lowercase						):
													for j_focus in range(0,					__lowercase,					__lowercase						):
																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(__lowercase						)						)
																elif pooling_type == "max_pooling":
																			# max pooling
																			map_pooled.append(np.max(__lowercase						)						)
										lowercase__					  =				np.asmatrix(__lowercase						).reshape(__lowercase,					__lowercase						)
										featuremap_pooled.append(__lowercase						)
							return featuremap_pooled




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




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Optional[int]						):
							# expanding matrix to one dimension list
							lowercase__					  =				np.asarray(__lowercase						)
							lowercase__					  =				np.shape(__lowercase						)
							lowercase__					  =				data_mat.reshape(1,					shapes[0] * shapes[1]						)
							return data_expanded




				def 							A__      (						self					:						str,					__lowercase					:						Tuple,					__lowercase					:						List[Any],					__lowercase					:						Any,					__lowercase					:						Union[str, Any],					__lowercase					:						Tuple						):
							lowercase__					  =				[]
							lowercase__					  =				0
							for i_map in range(__lowercase						):
										lowercase__					  =				np.ones((size_map, size_map)						)
										for i in range(0,					__lowercase,					__lowercase						):
													for j in range(0,					__lowercase,					__lowercase						):
																lowercase__					  =				pd_pool[
																    i_pool
																]
																lowercase__					  =				i_pool + 1
										lowercase__					  =				np.multiply(
										    __lowercase,					np.multiply(out_map[i_map],					(1 - out_map[i_map])						)						)
										pd_all.append(__lowercase						)
							return pd_all




				def 							A__      (						self					:						Tuple,					__lowercase					:						int,					__lowercase					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						List[str]=bool						):
							# model traning
							print("----------------------Start Training-------------------------"						)
							print((" - - Shape: Train_Data  ", np.shape(__lowercase						))						)
							print((" - - Shape: Teach_Data  ", np.shape(__lowercase						))						)
							lowercase__					  =				0
							lowercase__					  =				[]
							lowercase__					  =				1_0000
							while rp < n_repeat and mse >= error_accuracy:
										lowercase__					  =				0
										print(F'''-------------Learning Time {rp}--------------'''						)
										for p in range(len(__lowercase						)						):
													# print('------------Learning Image: %d--------------'%p)
													lowercase__					  =				np.asmatrix(datas_train[p]						)
													lowercase__					  =				np.asarray(datas_teach[p]						)
													lowercase__  ,       lowercase__					  =				self.convolute(
													    __lowercase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
													lowercase__					  =				self.pooling(__lowercase,					self.size_poolinga						)
													lowercase__					  =				np.shape(__lowercase						)
													lowercase__					  =				self._expand(__lowercase						)
													lowercase__					  =				data_bp_input

													lowercase__					  =				np.dot(__lowercase,					self.vji.T						) - self.thre_bpa
													lowercase__					  =				self.sig(__lowercase						)
													lowercase__					  =				np.dot(__lowercase,					self.wkj.T						) - self.thre_bpa
													lowercase__					  =				self.sig(__lowercase						)

													# --------------Model Leaning ------------------------
													# calculate error and gradient---------------
													lowercase__					  =				np.multiply(
													    (data_teach - bp_outa),					np.multiply(__lowercase,					(1 - bp_outa)						)						)
													lowercase__					  =				np.multiply(
													    np.dot(__lowercase,					self.wkj						),					np.multiply(__lowercase,					(1 - bp_outa)						)						)
													lowercase__					  =				np.dot(__lowercase,					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(
													    __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]						):
																lowercase__					  =				self._expand_mat(pd_conva_all[k_conv]						)
																lowercase__					  =				self.rate_weight * np.dot(__lowercase,					__lowercase						)

																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(__lowercase						)

							def draw_error():
										lowercase__					  =				[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					:						List[str],					__lowercase					:						Optional[int]						):
							# model predict
							lowercase__					  =				[]
							print("-------------------Start Testing-------------------------"						)
							print((" - - Shape: Test_Data  ", np.shape(__lowercase						))						)
							for p in range(len(__lowercase						)						):
										lowercase__					  =				np.asmatrix(datas_test[p]						)
										lowercase__  ,       lowercase__					  =				self.convolute(
										    __lowercase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
										lowercase__					  =				self.pooling(__lowercase,					self.size_poolinga						)
										lowercase__					  =				self._expand(__lowercase						)

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




				def 							A__      (						self					:						int,					__lowercase					:						Any						):
							# return the data of image after convoluting process so we can check it out
							lowercase__					  =				np.asmatrix(__lowercase						)
							lowercase__  ,       lowercase__					  =				self.convolute(
							    __lowercase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
							lowercase__					  =				self.pooling(__lowercase,					self.size_poolinga						)

							return data_conveda, data_pooleda


if __name__ == "__main__":
							pass




