import pickle

import numpy as np
from matplotlib import pyplot as plt




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[str]		,  _lowercase					:  List[Any]		,  _lowercase					:  Any		,  _lowercase					:  Tuple=0.2		,  _lowercase					:  Union[str, Any]=0.2							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= {
        '''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(lowerCAmelCase__		,  '''wb'''							) as f:
     pickle.dump(lowerCAmelCase__		,  lowerCAmelCase__							)

    print(f"""Model saved： {save_path}"""							)



   @classmethod
   def   lowerCAmelCase						(	cls					:  int		,  _lowercase					:  Dict							):



    """simple docstring"""



    with open(lowerCAmelCase__		,  '''rb'''							) as f:
     _UpperCamelCase:     Optional[int]							= pickle.load(lowerCAmelCase__							)  # noqa: S301

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



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  str							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  int							):



    """simple docstring"""



    return round(lowerCAmelCase__		,  3							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]		,  _lowercase					:  Tuple		,  _lowercase					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= convs[0]
    _UpperCamelCase:     Dict							= convs[1]
    _UpperCamelCase:     Dict							= np.shape(lowerCAmelCase__							)[0]
    # get the data slice of original image data, data_focus
    _UpperCamelCase:     str							= []
    for i_focus in range(0		,  size_data - size_conv + 1		,  lowerCAmelCase__							):
     for j_focus in range(0		,  size_data - size_conv + 1		,  lowerCAmelCase__							):
      _UpperCamelCase:     int							= data[
          i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
      ]
      data_focus.append(lowerCAmelCase__							)
       # calculate the feature map of every single kernel, and saved as list of matrix
    _UpperCamelCase:     Tuple							= []
    _UpperCamelCase:     Optional[int]							= int((size_data - size_conv) / conv_step + 1							)
    for i_map in range(lowerCAmelCase__							):
     _UpperCamelCase:     str							= []
     for i_focus in range(len(lowerCAmelCase__							)							):
      _UpperCamelCase:     Any							= (
          np.sum(np.multiply(data_focus[i_focus]		,  w_convs[i_map]							)							)
          - thre_convs[i_map]
      )
      featuremap.append(self.sig(lowerCAmelCase__							)							)
     _UpperCamelCase:     str							= np.asmatrix(lowerCAmelCase__							).reshape(
         lowerCAmelCase__		,  lowerCAmelCase__							)
     data_featuremap.append(lowerCAmelCase__							)

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



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Any		,  _lowercase					:  Optional[int]		,  _lowercase					:  Optional[int]="average_pool"							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= len(featuremaps[0]							)
    _UpperCamelCase:     Optional[Any]							= int(size_map / size_pooling							)
    _UpperCamelCase:     Tuple							= []
    for i_map in range(len(lowerCAmelCase__							)							):
     _UpperCamelCase:     Optional[Any]							= featuremaps[i_map]
     _UpperCamelCase:     Optional[int]							= []
     for i_focus in range(0		,  lowerCAmelCase__		,  lowerCAmelCase__							):
      for j_focus in range(0		,  lowerCAmelCase__		,  lowerCAmelCase__							):
       _UpperCamelCase:     Union[str, Any]							= 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(lowerCAmelCase__							)							)
       elif pooling_type == "max_pooling":
        # max pooling
        map_pooled.append(np.max(lowerCAmelCase__							)							)
     _UpperCamelCase:     Dict							= np.asmatrix(lowerCAmelCase__							).reshape(lowerCAmelCase__		,  lowerCAmelCase__							)
     featuremap_pooled.append(lowerCAmelCase__							)
    return featuremap_pooled



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  List[str]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= np.asarray(lowerCAmelCase__							)
    _UpperCamelCase:     Dict							= np.shape(lowerCAmelCase__							)
    _UpperCamelCase:     Dict							= data_mat.reshape(1		,  shapes[0] * shapes[1]							)
    return data_expanded



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Tuple		,  _lowercase					:  str		,  _lowercase					:  Optional[Any]		,  _lowercase					:  str		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  int		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[int]		,  _lowercase					:  Tuple		,  _lowercase					:  Tuple		,  _lowercase					:  Optional[int]=bool							):



    """simple docstring"""



    print('''----------------------Start Training-------------------------'''							)
    print((''' - - Shape: Train_Data  ''', np.shape(lowerCAmelCase__							))							)
    print((''' - - Shape: Teach_Data  ''', np.shape(lowerCAmelCase__							))							)
    _UpperCamelCase:     Tuple							= 0
    _UpperCamelCase:     List[Any]							= []
    _UpperCamelCase:     Any							= 10_000
    while rp < n_repeat and mse >= error_accuracy:
     _UpperCamelCase:     List[str]							= 0
     print(f"""-------------Learning Time {rp}--------------"""							)
     for p in range(len(lowerCAmelCase__							)							):
      # print('------------Learning Image: %d--------------'%p)
      _UpperCamelCase:     Optional[Any]							= np.asmatrix(datas_train[p]							)
      _UpperCamelCase:     int							= np.asarray(datas_teach[p]							)
      _UpperCamelCase	,							_UpperCamelCase:     int							= self.convolute(
          lowerCAmelCase__		,  self.conva		,  self.w_conva		,  self.thre_conva		,  conv_step=self.step_conva		,  )
      _UpperCamelCase:     List[str]							= self.pooling(lowerCAmelCase__		,  self.size_poolinga							)
      _UpperCamelCase:     Any							= np.shape(lowerCAmelCase__							)
      _UpperCamelCase:     List[Any]							= self._expand(lowerCAmelCase__							)
      _UpperCamelCase:     Union[str, Any]							= data_bp_input

      _UpperCamelCase:     Optional[Any]							= np.dot(lowerCAmelCase__		,  self.vji.T							) - self.thre_bpa
      _UpperCamelCase:     int							= self.sig(lowerCAmelCase__							)
      _UpperCamelCase:     str							= np.dot(lowerCAmelCase__		,  self.wkj.T							) - self.thre_bpa
      _UpperCamelCase:     int							= self.sig(lowerCAmelCase__							)

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

      _UpperCamelCase:     Optional[Any]							= pd_i_all / (self.size_poolinga * self.size_poolinga)
      _UpperCamelCase:     Optional[Any]							= pd_conva_pooled.T.getA().tolist()
      _UpperCamelCase:     Tuple							= self._calculate_gradient_from_pool(
          lowerCAmelCase__		,  lowerCAmelCase__		,  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:     List[str]							= self._expand_mat(pd_conva_all[k_conv]							)
       _UpperCamelCase:     int							= self.rate_weight * np.dot(lowerCAmelCase__		,  lowerCAmelCase__							)

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

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

    def draw_error():
     _UpperCamelCase:     int							= [error_accuracy for i in range(int(n_repeat * 1.2							)							)]
     plt.plot(lowerCAmelCase__		,  '''+-'''							)
     plt.plot(lowerCAmelCase__		,  '''r--'''							)
     plt.xlabel('''Learning Times'''							)
     plt.ylabel('''All_mse'''							)
     plt.grid(lowerCAmelCase__		,  alpha=0.5							)
     plt.show()

    print('''------------------Training Complished---------------------'''							)
    print((''' - - Training epoch: ''', rp, f"""     - - Mse: {mse:.6f}""")							)
    if draw_e:
     draw_error()
    return mse



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Dict							):



    """simple docstring"""



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

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



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



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

    return data_conveda, data_pooleda


if __name__ == "__main__":
  pass
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  str		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



    warnings.warn(
        '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use PerceiverImageProcessor instead.'''		,  _lowercase		,  )
    super().__init__(*_lowercase		,  **_lowercase							)