




import pickle

import numpy as np
from matplotlib import pyplot as plt







class 						A:



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					bp_numa
       lowerCamelCase_	  =					bp_numa
       lowerCamelCase_	  =					bp_numa
       lowerCamelCase_	  =					conva_get[:2]
       lowerCamelCase_	  =					conva_get[2]
       lowerCamelCase_	  =					size_pa
       lowerCamelCase_	  =					rate_w
       lowerCamelCase_	  =					rate_t
       lowerCamelCase_	  =					[
           np.mat(-1 * np.random.rand(self.conva[0]		,							self.conva[0]						) + 0.5						)
           for i in range(self.conva[1]						)
       ]
       lowerCamelCase_	  =					np.mat(-1 * np.random.rand(self.num_bpa		,							self.num_bpa						) + 0.5						)
       lowerCamelCase_	  =					np.mat(-1 * np.random.rand(self.num_bpa		,							self.num_bpa						) + 0.5						)
       lowerCamelCase_	  =					-2 * np.random.rand(self.conva[1]						) + 1
       lowerCamelCase_	  =					-2 * np.random.rand(self.num_bpa						) + 1
       lowerCamelCase_	  =					-2 * np.random.rand(self.num_bpa						) + 1

 def        a__				(       self      :						Optional[Any]		,							A_      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					{
           '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        a__				(       cls      :						int		,							A_      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       with open(A_		,							'rb'						) as f:
             lowerCamelCase_	  =					pickle.load(A_						)  # noqa: S301

       lowerCamelCase_	  =					model_dic.get('conv1'						)
       conv_get.append(model_dic.get('step_conv1'						)						)
       lowerCamelCase_	  =					model_dic.get('size_pooling1'						)
       lowerCamelCase_	  =					model_dic.get('num_bp1'						)
       lowerCamelCase_	  =					model_dic.get('num_bp2'						)
       lowerCamelCase_	  =					model_dic.get('num_bp3'						)
       lowerCamelCase_	  =					model_dic.get('rate_weight'						)
       lowerCamelCase_	  =					model_dic.get('rate_thre'						)
       # create model instance
       lowerCamelCase_	  =					CNN(A_		,							A_		,							A_		,							A_		,							A_		,							A_		,							A_						)
       # modify model parameter
       lowerCamelCase_	  =					model_dic.get('w_conv1'						)
       lowerCamelCase_	  =					model_dic.get('wkj'						)
       lowerCamelCase_	  =					model_dic.get('vji'						)
       lowerCamelCase_	  =					model_dic.get('thre_conv1'						)
       lowerCamelCase_	  =					model_dic.get('thre_bp2'						)
       lowerCamelCase_	  =					model_dic.get('thre_bp3'						)
       return conv_ins

 def        a__				(       self      :						Optional[int]		,							A_      :						Any						)     ->			List[str]:

       """simple docstring"""






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

 def        a__				(       self      :						Any		,							A_      :						Any						)     ->			Optional[Any]:

       """simple docstring"""






       return round(A_		,							3						)

 def        a__				(       self      :						int		,							A_      :						Optional[int]		,							A_      :						List[Any]		,							A_      :						Optional[int]		,							A_      :						Optional[Any]		,							A_      :						List[Any]						)     ->			Optional[int]:

       """simple docstring"""






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

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

 def        a__				(       self      :						Tuple		,							A_      :						int		,							A_      :						List[str]		,							A_      :						str="average_pool"						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					len(featuremaps[0]						)
       lowerCamelCase_	  =					int(size_map / size_pooling						)
       lowerCamelCase_	  =					[]
       for i_map in range(len(A_						)						):
             lowerCamelCase_	  =					featuremaps[i_map]
             lowerCamelCase_	  =					[]
             for i_focus in range(0		,							A_		,							A_						):
                   for j_focus in range(0		,							A_		,							A_						):
                         lowerCamelCase_	  =					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_						)						)
             lowerCamelCase_	  =					np.asmatrix(A_						).reshape(A_		,							A_						)
             featuremap_pooled.append(A_						)
       return featuremap_pooled

 def        a__				(       self      :						List[str]		,							A_      :						Optional[Any]						)     ->			Tuple:

       """simple docstring"""






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

 def        a__				(       self      :						Optional[Any]		,							A_      :						Dict						)     ->			int:

       """simple docstring"""






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

 def        a__				(       self      :						Union[str, Any]		,							A_      :						Optional[Any]		,							A_      :						Optional[Any]		,							A_      :						int		,							A_      :						int		,							A_      :						str						)     ->			Optional[int]:

       """simple docstring"""






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

 def        a__				(       self      :						Dict		,							A_      :						int		,							A_      :						Dict		,							A_      :						Dict		,							A_      :						int		,							A_      :						Tuple		,							A_      :						Dict=bool						)     ->			int:

       """simple docstring"""






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

                   lowerCamelCase_	  =					np.dot(A_		,							self.vji.T						) - self.thre_bpa
                   lowerCamelCase_	  =					self.sig(A_						)
                   lowerCamelCase_	  =					np.dot(A_		,							self.wkj.T						) - self.thre_bpa
                   lowerCamelCase_	  =					self.sig(A_						)

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

                   lowerCamelCase_	  =					pd_i_all / (self.size_poolinga * self.size_poolinga)
                   lowerCamelCase_	  =					pd_conva_pooled.T.getA().tolist()
                   lowerCamelCase_	  =					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]						):
                         lowerCamelCase_	  =					self._expand_mat(pd_conva_all[k_conv]						)
                         lowerCamelCase_	  =					self.rate_weight * np.dot(A_		,							A_						)

                         lowerCamelCase_	  =					self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])						)

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

       def draw_error():
             lowerCamelCase_	  =					[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        a__				(       self      :						Any		,							A_      :						str						)     ->			int:

       """simple docstring"""






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

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





 def        a__				(       self      :						Dict		,							A_      :						str						)     ->			Any:

       """simple docstring"""






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

       return data_conveda, data_pooleda


if __name__ == "__main__":
 pass









import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



