



'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


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






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

    for issue in open_issues:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= sorted(issue.get_comments()    ,	key=lambda _SCREAMING_SNAKE_CASE       : i.created_at    ,	reverse=_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= comments[0] if len(_SCREAMING_SNAKE_CASE       ) > 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()









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=0.2,					__UpperCAmelCase : Dict=0.2      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''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 				lowercase__  (cls : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					with open(__UpperCAmelCase,					'''rb'''      ) as f:
									SCREAMING_SNAKE_CASE  :						int      					= pickle.load(__UpperCAmelCase      )  # noqa: S301

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


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

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

					# expanding the data slice to One dimenssion
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					for each_focus in data_focus:
									focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asarray(__UpperCAmelCase      )
					return focus_list, data_featuremap


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(featuremaps[0]      )
					SCREAMING_SNAKE_CASE  :						str      					= int(size_map / size_pooling      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					for i_map in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						str      					= featuremaps[i_map]
									SCREAMING_SNAKE_CASE  :						Any      					= []
									for i_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= 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      )      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase,					__UpperCAmelCase      )
									featuremap_pooled.append(__UpperCAmelCase      )
					return featuremap_pooled


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= []
					for i in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.shape(data[i]      )
									SCREAMING_SNAKE_CASE  :						str      					= data[i].reshape(1,					shapes[0] * shapes[1]      )
									SCREAMING_SNAKE_CASE  :						int      					= data_listed.getA().tolist()[0]
									data_expanded.extend(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.asarray(__UpperCAmelCase      )
					return data_expanded


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

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

													SCREAMING_SNAKE_CASE  :						Tuple      					= np.dot(__UpperCAmelCase,					self.vji.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.sig(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.wkj.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )

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

													SCREAMING_SNAKE_CASE  :						Dict      					= pd_i_all / (self.size_poolinga * self.size_poolinga)
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pd_conva_pooled.T.getA().tolist()
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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]      ):
																	SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._expand_mat(pd_conva_all[k_conv]      )
																	SCREAMING_SNAKE_CASE  :						Any      					= self.rate_weight * np.dot(__UpperCAmelCase,					__UpperCAmelCase      )

																	SCREAMING_SNAKE_CASE  :						List[Any]      					= self.w_conva[k_conv] + delta_w.reshape(
																	    (self.conva[0], self.conva[0])      )

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

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [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 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

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

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






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convolute(
					    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




