'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=0.2    ,     _lowerCamelCase=0.2 ):
     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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # save model dict with pickle
     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(_lowercase    ,     '''wb''' ) as f:
        pickle.dump(_lowercase    ,     _lowercase )

     print(F'''Model saved： {save_path}''' )





  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase ):
     # read saved model
     with open(_lowercase    ,     '''rb''' ) as f:
        lowerCAmelCase_			=      pickle.load(_lowercase )  # 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(_lowercase    ,     _lowercase    ,     _lowercase    ,     _lowercase    ,     _lowercase    ,     _lowercase    ,     _lowercase )
     # 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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return 1 / (1 + np.exp(-1 * x ))





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return round(_lowercase    ,     3 )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     # convolution process
     lowerCAmelCase_			=      convs[0]
     lowerCAmelCase_			=      convs[1]
     lowerCAmelCase_			=      np.shape(_lowercase )[0]
     # get the data slice of original image data, data_focus
     lowerCAmelCase_			=      []
     for i_focus in range(0    ,     size_data - size_conv + 1    ,     _lowercase ):
        for j_focus in range(0    ,     size_data - size_conv + 1    ,     _lowercase ):
           lowerCAmelCase_			=      data[
               i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
           ]
           data_focus.append(_lowercase )
        # 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(_lowercase ):
        lowerCAmelCase_			=      []
        for i_focus in range(len(_lowercase ) ):
           lowerCAmelCase_			=      (
               np.sum(np.multiply(data_focus[i_focus]    ,     w_convs[i_map] ) )
               - thre_convs[i_map]
           )
           featuremap.append(self.sig(_lowercase ) )
        lowerCAmelCase_			=      np.asmatrix(_lowercase ).reshape(
            _lowercase    ,     _lowercase )
        data_featuremap.append(_lowercase )

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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase="average_pool" ):
     # pooling process
     lowerCAmelCase_			=      len(featuremaps[0] )
     lowerCAmelCase_			=      int(size_map / size_pooling )
     lowerCAmelCase_			=      []
     for i_map in range(len(_lowercase ) ):
        lowerCAmelCase_			=      featuremaps[i_map]
        lowerCAmelCase_			=      []
        for i_focus in range(0    ,     _lowercase    ,     _lowercase ):
           for j_focus in range(0    ,     _lowercase    ,     _lowercase ):
              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(_lowercase ) )
              elif pooling_type == "max_pooling":
                 # max pooling
                 map_pooled.append(np.max(_lowercase ) )
        lowerCAmelCase_			=      np.asmatrix(_lowercase ).reshape(_lowercase    ,     _lowercase )
        featuremap_pooled.append(_lowercase )
     return featuremap_pooled





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # expanding three dimension data to one dimension list
     lowerCAmelCase_			=      []
     for i in range(len(_lowercase ) ):
        lowerCAmelCase_			=      np.shape(data[i] )
        lowerCAmelCase_			=      data[i].reshape(1    ,     shapes[0] * shapes[1] )
        lowerCAmelCase_			=      data_listed.getA().tolist()[0]
        data_expanded.extend(_lowercase )
     lowerCAmelCase_			=      np.asarray(_lowercase )
     return data_expanded





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # expanding matrix to one dimension list
     lowerCAmelCase_			=      np.asarray(_lowercase )
     lowerCAmelCase_			=      np.shape(_lowercase )
     lowerCAmelCase_			=      data_mat.reshape(1    ,     shapes[0] * shapes[1] )
     return data_expanded





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      0
     for i_map in range(_lowercase ):
        lowerCAmelCase_			=      np.ones((size_map, size_map) )
        for i in range(0    ,     _lowercase    ,     _lowercase ):
           for j in range(0    ,     _lowercase    ,     _lowercase ):
              lowerCAmelCase_			=      pd_pool[
                  i_pool
              ]
              lowerCAmelCase_			=      i_pool + 1
        lowerCAmelCase_			=      np.multiply(
            _lowercase    ,     np.multiply(out_map[i_map]    ,     (1 - out_map[i_map]) ) )
        pd_all.append(_lowercase )
     return pd_all





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

           lowerCAmelCase_			=      np.dot(_lowercase    ,     self.vji.T ) - self.thre_bpa
           lowerCAmelCase_			=      self.sig(_lowercase )
           lowerCAmelCase_			=      np.dot(_lowercase    ,     self.wkj.T ) - self.thre_bpa
           lowerCAmelCase_			=      self.sig(_lowercase )

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

              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(_lowercase )

     def draw_error():
        lowerCAmelCase_			=      [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
        plt.plot(_lowercase    ,     '''+-''' )
        plt.plot(_lowercase    ,     '''r--''' )
        plt.xlabel('''Learning Times''' )
        plt.ylabel('''All_mse''' )
        plt.grid(_lowercase    ,     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    ,     _lowerCamelCase ):
     # model predict
     lowerCAmelCase_			=      []
     print('''-------------------Start Testing-------------------------''' )
     print((''' - - Shape: Test_Data  ''', np.shape(_lowercase )) )
     for p in range(len(_lowercase ) ):
        lowerCAmelCase_			=      np.asmatrix(datas_test[p] )
        lowerCAmelCase_	,lowerCAmelCase_			=      self.convolute(
            _lowercase    ,     self.conva    ,     self.w_conva    ,     self.thre_conva    ,     conv_step=self.step_conva    ,     )
        lowerCAmelCase_			=      self.pooling(_lowercase    ,     self.size_poolinga )
        lowerCAmelCase_			=      self._expand(_lowercase )

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







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # return the data of image after convoluting process so we can check it out
     lowerCAmelCase_			=      np.asmatrix(_lowercase )
     lowerCAmelCase_	,lowerCAmelCase_			=      self.convolute(
         _lowercase    ,     self.conva    ,     self.w_conva    ,     self.thre_conva    ,     conv_step=self.step_conva    ,     )
     lowerCAmelCase_			=      self.pooling(_lowercase    ,     self.size_poolinga )

     return data_conveda, data_pooleda


if __name__ == "__main__":
 pass











'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




