


import pickle

import numpy as np
from matplotlib import pyplot as plt



class 	__lowerCamelCase			:







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






            '''simple docstring'''




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







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			{
                '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 		snake_case_	(			cls: Union[str, Any],A_: int					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: Optional[Any],A_: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Tuple,A_: Tuple					):






            '''simple docstring'''




            return round(A_,3					)







       def 		snake_case_	(			self: Dict,A_: Tuple,A_: Any,A_: Union[str, Any],A_: Optional[Any],A_: Any					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: List[str],A_: List[str],A_: Dict,A_: Any="average_pool"					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str],A_: Optional[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any,A_: Optional[int]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: int,A_: str,A_: int,A_: Optional[int],A_: Any,A_: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Tuple,A_: Union[str, Any],A_: str,A_: int,A_: Union[str, Any],A_: str,A_: Tuple=bool					):






            '''simple docstring'''




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

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

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

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

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

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

            def draw_error():
                 __UpperCamelCase =			[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 		snake_case_	(			self: Optional[Any],A_: List[Any]					):






            '''simple docstring'''




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

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







       def 		snake_case_	(			self: Tuple,A_: Union[str, Any]					):






            '''simple docstring'''




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

            return data_conveda, data_pooleda


if __name__ == "__main__":
    pass






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets



class 	__lowerCamelCase			(_a						):







       @slow
       @require_torch
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny','prajjwal1/bert-tiny'					)
            __UpperCamelCase =			BertTokenizer.from_pretrained('bert-base-uncased'					)

            __UpperCamelCase =			bertabert.config.encoder.vocab_size
            __UpperCamelCase =			tokenizer.sep_token_id
            __UpperCamelCase =			tokenizer.cls_token_id
            __UpperCamelCase =			128

            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='train[:1%]'					)
            __UpperCamelCase =			datasets.load_dataset('cnn_dailymail','3.0.0',split='validation[:1%]'					)

            __UpperCamelCase =			train_dataset.select(range(32					)					)
            __UpperCamelCase =			val_dataset.select(range(16					)					)

            __UpperCamelCase =			4

            def _map_to_encoder_decoder_inputs(A_: Dict					):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 __UpperCamelCase =			tokenizer(batch['article'],padding='max_length',truncation=A_,max_length=512					)
                 __UpperCamelCase =			tokenizer(batch['highlights'],padding='max_length',truncation=A_,max_length=128					)
                 __UpperCamelCase =			inputs.input_ids
                 __UpperCamelCase =			inputs.attention_mask

                 __UpperCamelCase =			outputs.input_ids
                 __UpperCamelCase =			outputs.input_ids.copy()
                 __UpperCamelCase =			[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 __UpperCamelCase =			outputs.attention_mask

                 assert all(len(A_					) == 512 for x in inputs.input_ids					)
                 assert all(len(A_					) == 128 for x in outputs.input_ids					)

                 return batch

            def _compute_metrics(A_: str					):
                 __UpperCamelCase =			pred.label_ids
                 __UpperCamelCase =			pred.predictions

                 # all unnecessary tokens are removed
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)
                 __UpperCamelCase =			tokenizer.batch_decode(A_,skip_special_tokens=A_					)

                 __UpperCamelCase =			sum([int(pred_str[i] == label_str[i]					) for i in range(len(A_					)					)]					) / len(A_					)

                 return {"accuracy": accuracy}

            # map train dataset
            __UpperCamelCase =			train_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            train_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            # same for validation dataset
            __UpperCamelCase =			val_dataset.map(
                _map_to_encoder_decoder_inputs,batched=A_,batch_size=A_,remove_columns=['article', 'highlights'],)
            val_dataset.set_format(
                type='torch',columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'],)

            __UpperCamelCase =			self.get_auto_remove_tmp_dir()

            __UpperCamelCase =			SeqaSeqTrainingArguments(
                output_dir=A_,per_device_train_batch_size=A_,per_device_eval_batch_size=A_,predict_with_generate=A_,evaluation_strategy='steps',do_train=A_,do_eval=A_,warmup_steps=0,eval_steps=2,logging_steps=2,)

            # instantiate trainer
            __UpperCamelCase =			SeqaSeqTrainer(
                model=A_,args=A_,compute_metrics=_compute_metrics,train_dataset=A_,eval_dataset=A_,tokenizer=A_,)

            # start training
            trainer.train()


