




import pickle

import numpy as np
from matplotlib import pyplot as plt

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase=0.2 ,       __lowercase=0.2  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  bp_numa
									a__       :						Optional[int]							=  bp_numa
									a__       :						str							=  bp_numa
									a__       :						Optional[int]							=  conva_get[:2]
									a__       :						Tuple							=  conva_get[2]
									a__       :						str							=  size_pa
									a__       :						List[str]							=  rate_w
									a__       :						Optional[int]							=  rate_t
									a__       :						Optional[int]							=  [
									    np.mat(-1 * np.random.rand(self.conva[0] ,       self.conva[0]  ) + 0.5  )
									    for i in range(self.conva[1]  )
									]
									a__       :						str							=  np.mat(-1 * np.random.rand(self.num_bpa ,       self.num_bpa  ) + 0.5  )
									a__       :						int							=  np.mat(-1 * np.random.rand(self.num_bpa ,       self.num_bpa  ) + 0.5  )
									a__       :						Tuple							=  -2 * np.random.rand(self.conva[1]  ) + 1
									a__       :						Tuple							=  -2 * np.random.rand(self.num_bpa  ) + 1
									a__       :						int							=  -2 * np.random.rand(self.num_bpa  ) + 1

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Tuple							=  {
									    "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 							SCREAMING_SNAKE_CASE__(       cls ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									with open(UpperCAmelCase_ ,       """rb"""  ) as f:
												a__       :						Tuple							=  pickle.load(UpperCAmelCase_  )  # noqa: S301

									a__       :						Any							=  model_dic.get("""conv1"""  )
									conv_get.append(model_dic.get("""step_conv1"""  )  )
									a__       :						Tuple							=  model_dic.get("""size_pooling1"""  )
									a__       :						Optional[int]							=  model_dic.get("""num_bp1"""  )
									a__       :						Optional[int]							=  model_dic.get("""num_bp2"""  )
									a__       :						List[Any]							=  model_dic.get("""num_bp3"""  )
									a__       :						Any							=  model_dic.get("""rate_weight"""  )
									a__       :						str							=  model_dic.get("""rate_thre"""  )
									# create model instance
									a__       :						Optional[int]							=  CNN(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_  )
									# modify model parameter
									a__       :						str							=  model_dic.get("""w_conv1"""  )
									a__       :						Optional[Any]							=  model_dic.get("""wkj"""  )
									a__       :						Union[str, Any]							=  model_dic.get("""vji"""  )
									a__       :						List[Any]							=  model_dic.get("""thre_conv1"""  )
									a__       :						Union[str, Any]							=  model_dic.get("""thre_bp2"""  )
									a__       :						int							=  model_dic.get("""thre_bp3"""  )
									return conv_ins

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									return round(UpperCAmelCase_ ,       3  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				int:
									"""simple docstring"""




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

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase="average_pool"  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  len(featuremaps[0]  )
									a__       :						Any							=  int(size_map / size_pooling  )
									a__       :						Optional[int]							=  []
									for i_map in range(len(UpperCAmelCase_  )  ):
												a__       :						Dict							=  featuremaps[i_map]
												a__       :						Tuple							=  []
												for i_focus in range(0 ,       UpperCAmelCase_ ,       UpperCAmelCase_  ):
															for j_focus in range(0 ,       UpperCAmelCase_ ,       UpperCAmelCase_  ):
																		a__       :						List[Any]							=  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_  )  )
												a__       :						List[Any]							=  np.asmatrix(UpperCAmelCase_  ).reshape(UpperCAmelCase_ ,       UpperCAmelCase_  )
												featuremap_pooled.append(UpperCAmelCase_  )
									return featuremap_pooled

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  []
									for i in range(len(UpperCAmelCase_  )  ):
												a__       :						Dict							=  np.shape(data[i]  )
												a__       :						Optional[Any]							=  data[i].reshape(1 ,       shapes[0] * shapes[1]  )
												a__       :						Union[str, Any]							=  data_listed.getA().tolist()[0]
												data_expanded.extend(UpperCAmelCase_  )
									a__       :						Optional[Any]							=  np.asarray(UpperCAmelCase_  )
									return data_expanded

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						List[str]							=  np.asarray(UpperCAmelCase_  )
									a__       :						Dict							=  np.shape(UpperCAmelCase_  )
									a__       :						Any							=  data_mat.reshape(1 ,       shapes[0] * shapes[1]  )
									return data_expanded

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase=bool  ) ->				List[Any]:
									"""simple docstring"""




									print("""----------------------Start Training-------------------------"""  )
									print((""" - - Shape: Train_Data  """, np.shape(UpperCAmelCase_  ))  )
									print((""" - - Shape: Teach_Data  """, np.shape(UpperCAmelCase_  ))  )
									a__       :						Tuple							=  0
									a__       :						Optional[Any]							=  []
									a__       :						List[Any]							=  1_0_0_0_0
									while rp < n_repeat and mse >= error_accuracy:
												a__       :						List[Any]							=  0
												print(F'''-------------Learning Time {rp}--------------'''  )
												for p in range(len(UpperCAmelCase_  )  ):
															# print('------------Learning Image: %d--------------'%p)
															a__       :						List[Any]							=  np.asmatrix(datas_train[p]  )
															a__       :						str							=  np.asarray(datas_teach[p]  )
															a__       :						Dict							=  self.convolute(
															    UpperCAmelCase_ ,       self.conva ,       self.w_conva ,       self.thre_conva ,       conv_step=self.step_conva ,       )
															a__       :						Tuple							=  self.pooling(UpperCAmelCase_ ,       self.size_poolinga  )
															a__       :						Optional[Any]							=  np.shape(UpperCAmelCase_  )
															a__       :						Optional[int]							=  self._expand(UpperCAmelCase_  )
															a__       :						Optional[Any]							=  data_bp_input

															a__       :						Dict							=  np.dot(UpperCAmelCase_ ,       self.vji.T  ) - self.thre_bpa
															a__       :						Union[str, Any]							=  self.sig(UpperCAmelCase_  )
															a__       :						int							=  np.dot(UpperCAmelCase_ ,       self.wkj.T  ) - self.thre_bpa
															a__       :						Tuple							=  self.sig(UpperCAmelCase_  )

															# --------------Model Leaning ------------------------
															# calculate error and gradient---------------
															a__       :						List[str]							=  np.multiply(
															    (data_teach - bp_outa) ,       np.multiply(UpperCAmelCase_ ,       (1 - bp_outa)  )  )
															a__       :						Union[str, Any]							=  np.multiply(
															    np.dot(UpperCAmelCase_ ,       self.wkj  ) ,       np.multiply(UpperCAmelCase_ ,       (1 - bp_outa)  )  )
															a__       :						Optional[int]							=  np.dot(UpperCAmelCase_ ,       self.vji  )

															a__       :						int							=  pd_i_all / (self.size_poolinga * self.size_poolinga)
															a__       :						Union[str, Any]							=  pd_conva_pooled.T.getA().tolist()
															a__       :						str							=  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]  ):
																		a__       :						Optional[int]							=  self._expand_mat(pd_conva_all[k_conv]  )
																		a__       :						Union[str, Any]							=  self.rate_weight * np.dot(UpperCAmelCase_ ,       UpperCAmelCase_  )

																		a__       :						int							=  self.w_conva[k_conv] + delta_w.reshape(
																		    (self.conva[0], self.conva[0])  )

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

									def draw_error():
												a__       :						Union[str, Any]							=  [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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Dict							=  []
									print("""-------------------Start Testing-------------------------"""  )
									print((""" - - Shape: Test_Data  """, np.shape(UpperCAmelCase_  ))  )
									for p in range(len(UpperCAmelCase_  )  ):
												a__       :						Optional[int]							=  np.asmatrix(datas_test[p]  )
												a__       :						Union[str, Any]							=  self.convolute(
												    UpperCAmelCase_ ,       self.conva ,       self.w_conva ,       self.thre_conva ,       conv_step=self.step_conva ,       )
												a__       :						Any							=  self.pooling(UpperCAmelCase_ ,       self.size_poolinga  )
												a__       :						List[Any]							=  self._expand(UpperCAmelCase_  )

												a__       :						List[Any]							=  data_bp_input
												a__       :						int							=  bp_outa * self.vji.T - self.thre_bpa
												a__       :						Union[str, Any]							=  self.sig(UpperCAmelCase_  )
												a__       :						Optional[Any]							=  bp_outa * self.wkj.T - self.thre_bpa
												a__       :						str							=  self.sig(UpperCAmelCase_  )
												produce_out.extend(bp_outa.getA().tolist()  )
									a__       :						int							=  [list(map(self.do_round ,       UpperCAmelCase_  )  ) for each in produce_out]
									return np.asarray(UpperCAmelCase_  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						Any							=  np.asmatrix(UpperCAmelCase_  )
									a__       :						Any							=  self.convolute(
									    UpperCAmelCase_ ,       self.conva ,       self.w_conva ,       self.thre_conva ,       conv_step=self.step_conva ,       )
									a__       :						Optional[int]							=  self.pooling(UpperCAmelCase_ ,       self.size_poolinga  )

									return data_conveda, data_pooleda


if __name__ == "__main__":
		pass









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




