


import pickle

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





     '''simple docstring'''



     def __init__(					self:List[str]     ,		_a:str     ,		_a:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Optional[Any]     ,		_a:int=0.2     ,		_a:Optional[int]=0.2   ):
       snake_case__						= bp_numa
       snake_case__						= bp_numa
       snake_case__						= bp_numa
       snake_case__						= conva_get[:2]
       snake_case__						= conva_get[2]
       snake_case__						= size_pa
       snake_case__						= rate_w
       snake_case__						= rate_t
       snake_case__						= [
           np.mat(-1 * np.random.rand(self.conva[0]     ,		self.conva[0]   ) + 0.5   )
           for i in range(self.conva[1]   )
       ]
       snake_case__						= np.mat(-1 * np.random.rand(self.num_bpa     ,		self.num_bpa   ) + 0.5   )
       snake_case__						= np.mat(-1 * np.random.rand(self.num_bpa     ,		self.num_bpa   ) + 0.5   )
       snake_case__						= -2 * np.random.rand(self.conva[1]   ) + 1
       snake_case__						= -2 * np.random.rand(self.num_bpa   ) + 1
       snake_case__						= -2 * np.random.rand(self.num_bpa   ) + 1

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[Any]   ):
       # save model dict with pickle
       snake_case__						= {
           '''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 	SCREAMING_SNAKE_CASE__						(					cls:Any     ,		_a:Tuple   ):
       # read saved model
       with open(_a     ,		'''rb'''   ) as f:
         snake_case__						= pickle.load(_a   )  # noqa: S301

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int   ):
       return 1 / (1 + np.exp(-1 * x   ))

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:Dict   ):
       return round(_a     ,		3   )

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[str]     ,		_a:str     ,		_a:List[Any]="average_pool"   ):
       # pooling process
       snake_case__						= len(featuremaps[0]   )
       snake_case__						= int(size_map / size_pooling   )
       snake_case__						= []
       for i_map in range(len(_a   )   ):
         snake_case__						= featuremaps[i_map]
         snake_case__						= []
         for i_focus in range(0     ,		_a     ,		_a   ):
           for j_focus in range(0     ,		_a     ,		_a   ):
             snake_case__						= 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   )   )
         snake_case__						= np.asmatrix(_a   ).reshape(_a     ,		_a   )
         featuremap_pooled.append(_a   )
       return featuremap_pooled

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

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Optional[Any]   ):
       # expanding matrix to one dimension list
       snake_case__						= np.asarray(_a   )
       snake_case__						= np.shape(_a   )
       snake_case__						= data_mat.reshape(1     ,		shapes[0] * shapes[1]   )
       return data_expanded

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Dict     ,		_a:List[str]     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:Any     ,		_a:Dict=bool   ):
       # model traning
       print('''----------------------Start Training-------------------------'''   )
       print((''' - - Shape: Train_Data  ''', np.shape(_a   ))   )
       print((''' - - Shape: Teach_Data  ''', np.shape(_a   ))   )
       snake_case__						= 0
       snake_case__						= []
       snake_case__						= 1_00_00
       while rp < n_repeat and mse >= error_accuracy:
         snake_case__						= 0
         print(F"""-------------Learning Time {rp}--------------"""   )
         for p in range(len(_a   )   ):
           # print('------------Learning Image: %d--------------'%p)
           snake_case__						= np.asmatrix(datas_train[p]   )
           snake_case__						= np.asarray(datas_teach[p]   )
           snake_case__				,				snake_case__						= self.convolute(
               _a     ,		self.conva     ,		self.w_conva     ,		self.thre_conva     ,		conv_step=self.step_conva     ,		)
           snake_case__						= self.pooling(_a     ,		self.size_poolinga   )
           snake_case__						= np.shape(_a   )
           snake_case__						= self._expand(_a   )
           snake_case__						= data_bp_input

           snake_case__						= np.dot(_a     ,		self.vji.T   ) - self.thre_bpa
           snake_case__						= self.sig(_a   )
           snake_case__						= np.dot(_a     ,		self.wkj.T   ) - self.thre_bpa
           snake_case__						= self.sig(_a   )

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

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

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

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

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

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







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:str   ):
       # return the data of image after convoluting process so we can check it out
       snake_case__						= np.asmatrix(_a   )
       snake_case__				,				snake_case__						= self.convolute(
           _a     ,		self.conva     ,		self.w_conva     ,		self.thre_conva     ,		conv_step=self.step_conva     ,		)
       snake_case__						= self.pooling(_a     ,		self.size_poolinga   )

       return data_conveda, data_pooleda


if __name__ == "__main__":
   pass





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							str	   =       'AutoImageProcessor'
     __lowercase				:							Dict	   =       'AutoTokenizer'

     def __init__(					self:int     ,		_a:List[str]=None     ,		_a:Optional[Any]=None     ,		**_a:List[str]   ):
       snake_case__						= None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''     ,		_a     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''   )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''   )

       super().__init__(_a     ,		_a   )
       snake_case__						= self.image_processor
       snake_case__						= False

     def __call__(					self:Optional[int]     ,		*_a:str     ,		**_a:int   ):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*_a     ,		**_a   )

       snake_case__						= kwargs.pop('''images'''     ,		_a   )
       snake_case__						= kwargs.pop('''text'''     ,		_a   )
       if len(_a   ) > 0:
         snake_case__						= args[0]
         snake_case__						= args[1:]

       if images is None and text is None:
         raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

       if images is not None:
         snake_case__						= self.image_processor(_a     ,		*_a     ,		**_a   )
       if text is not None:
         snake_case__						= self.tokenizer(_a     ,		**_a   )

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         snake_case__						= encodings['''input_ids''']
         return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		*_a:Union[str, Any]     ,		**_a:Any   ):
       return self.tokenizer.batch_decode(*_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		*_a:Union[str, Any]     ,		**_a:Optional[int]   ):
       return self.tokenizer.decode(*_a     ,		**_a   )

     @contextmanager
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your images inputs, or in a separate call.'''   )
       snake_case__						= True
       snake_case__						= self.tokenizer
       yield
       snake_case__						= self.image_processor
       snake_case__						= False

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Dict     ,		_a:Dict=False     ,		_a:Optional[int]=None   ):
       if added_vocab is None:
         snake_case__						= self.tokenizer.get_added_vocab()

       snake_case__						= {}

       while tokens:
         snake_case__						= re.search(r'''<s_(.*?)>'''     ,		_a     ,		re.IGNORECASE   )
         if start_token is None:
           break
         snake_case__						= start_token.group(1   )
         snake_case__						= re.search(rF"""</s_{key}>"""     ,		_a     ,		re.IGNORECASE   )
         snake_case__						= start_token.group()
         if end_token is None:
           snake_case__						= tokens.replace(_a     ,		''''''   )
         else:
           snake_case__						= end_token.group()
           snake_case__						= re.escape(_a   )
           snake_case__						= re.escape(_a   )
           snake_case__						= re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""     ,		_a     ,		re.IGNORECASE   )
           if content is not None:
             snake_case__						= content.group(1   ).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               snake_case__						= self.tokenajson(_a     ,		is_inner_value=_a     ,		added_vocab=_a   )
               if value:
                 if len(_a   ) == 1:
                   snake_case__						= value[0]
                 snake_case__						= value
             else:  # leaf nodes
               snake_case__						= []
               for leaf in content.split(r'''<sep/>'''   ):
                 snake_case__						= leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   snake_case__						= leaf[1:-2]  # for categorical special tokens
                 output[key].append(_a   )
               if len(output[key]   ) == 1:
                 snake_case__						= output[key][0]

           snake_case__						= tokens[tokens.find(_a   ) + len(_a   ) :].strip()
           if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:]     ,		is_inner_value=_a     ,		added_vocab=_a   )

       if len(_a   ):
         return [output] if is_inner_value else output
       else:
         return [] if is_inner_value else {"text_sequence": tokens}

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		_a     ,		)
       return self.image_processor_class







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		_a     ,		)
       return self.image_processor

