




'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt





class    __lowercase						:







			def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0.2      )				->    Dict:
					__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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
					# 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(UpperCamelCase      ,							'wb'      ) as f:
							pickle.dump(UpperCamelCase      ,							UpperCamelCase      )

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







			@classmethod
			def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      )				->    Optional[int]:
					# read saved model
					with open(UpperCamelCase      ,							'rb'      ) as f:
							__a			=					pickle.load(UpperCamelCase      )  # 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(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )
					# 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
					return 1 / (1 + np.exp(-1 * x      ))







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
					return round(UpperCamelCase      ,							3      )







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

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







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase="average_pool"      )				->    Optional[Any]:
					# pooling process
					__a			=					len(featuremaps[0]      )
					__a			=					int(size_map / size_pooling      )
					__a			=					[]
					for i_map in range(len(UpperCamelCase      )      ):
							__a			=					featuremaps[i_map]
							__a			=					[]
							for i_focus in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
									for j_focus in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
											__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(UpperCamelCase      )      )
											elif pooling_type == "max_pooling":
													# max pooling
													map_pooled.append(np.max(UpperCamelCase      )      )
							__a			=					np.asmatrix(UpperCamelCase      ).reshape(UpperCamelCase      ,							UpperCamelCase      )
							featuremap_pooled.append(UpperCamelCase      )
					return featuremap_pooled







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







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict:
					# expanding matrix to one dimension list
					__a			=					np.asarray(UpperCamelCase      )
					__a			=					np.shape(UpperCamelCase      )
					__a			=					data_mat.reshape(1      ,							shapes[0] * shapes[1]      )
					return data_expanded







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[str]:
					__a			=					[]
					__a			=					0
					for i_map in range(UpperCamelCase      ):
							__a			=					np.ones((size_map, size_map)      )
							for i in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
									for j in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
											__a			=					pd_pool[
											    i_pool
											]
											__a			=					i_pool + 1
							__a			=					np.multiply(
							    UpperCamelCase      ,							np.multiply(out_map[i_map]      ,							(1 - out_map[i_map])      )      )
							pd_all.append(UpperCamelCase      )
					return pd_all







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

									__a			=					np.dot(UpperCamelCase      ,							self.vji.T      ) - self.thre_bpa
									__a			=					self.sig(UpperCamelCase      )
									__a			=					np.dot(UpperCamelCase      ,							self.wkj.T      ) - self.thre_bpa
									__a			=					self.sig(UpperCamelCase      )

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

											__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(UpperCamelCase      )

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

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







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
					# return the data of image after convoluting process so we can check it out
					__a			=					np.asmatrix(UpperCamelCase      )
					__a	,       __a			=					self.convolute(
					    UpperCamelCase      ,							self.conva      ,							self.w_conva      ,							self.thre_conva      ,							conv_step=self.step_conva      ,							)
					__a			=					self.pooling(UpperCamelCase      ,							self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
		pass









'''simple docstring'''


import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase_ 		=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					Github(os.environ['GITHUB_TOKEN']			)
  __a			=					g.get_repo('huggingface/diffusers'			)
  __a			=					repo.get_issues(state='open'			)

  for issue in open_issues:
    __a			=					sorted(issue.get_comments()				,					key=lambda a_			: i.created_at				,					reverse=a_			)
    __a			=					comments[0] if len(a_			) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='closed'			)
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='open'			)
      issue.remove_from_labels('stale'			)
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          'This issue has been automatically marked as stale because it has not had '
          'recent activity. If you think this still needs to be addressed '
          'please comment on this thread.\n\nPlease note that issues that do not follow the '
          '[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
          'are likely to be ignored.'			)
      issue.add_to_labels('stale'			)


if __name__ == "__main__":
  main()



