




'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt





class    __lowercase						:







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0.2      )				->    Dict:
     __a			=					bp_numa
     __a			=					bp_numa
     __a			=					bp_numa
     __a			=					conva_get[:2]
     __a			=					conva_get[2]
     __a			=					size_pa
     __a			=					rate_w
     __a			=					rate_t
     __a			=					[
         np.mat(-1 * np.random.rand(self.conva[0]      ,							self.conva[0]      ) + 0.5      )
         for i in range(self.conva[1]      )
     ]
     __a			=					np.mat(-1 * np.random.rand(self.num_bpa      ,							self.num_bpa      ) + 0.5      )
     __a			=					np.mat(-1 * np.random.rand(self.num_bpa      ,							self.num_bpa      ) + 0.5      )
     __a			=					-2 * np.random.rand(self.conva[1]      ) + 1
     __a			=					-2 * np.random.rand(self.num_bpa      ) + 1
     __a			=					-2 * np.random.rand(self.num_bpa      ) + 1







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
     # save model dict with pickle
     __a			=					{
         '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(UpperCamelCase      ,							'wb'      ) as f:
       pickle.dump(UpperCamelCase      ,							UpperCamelCase      )

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







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      )				->    Optional[int]:
     # read saved model
     with open(UpperCamelCase      ,							'rb'      ) as f:
       __a			=					pickle.load(UpperCamelCase      )  # noqa: S301

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     return 1 / (1 + np.exp(-1 * x      ))







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     return round(UpperCamelCase      ,							3      )







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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase="average_pool"      )				->    Optional[Any]:
     # pooling process
     __a			=					len(featuremaps[0]      )
     __a			=					int(size_map / size_pooling      )
     __a			=					[]
     for i_map in range(len(UpperCamelCase      )      ):
       __a			=					featuremaps[i_map]
       __a			=					[]
       for i_focus in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
         for j_focus in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
           __a			=					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(UpperCamelCase      )      )
           elif pooling_type == "max_pooling":
             # max pooling
             map_pooled.append(np.max(UpperCamelCase      )      )
       __a			=					np.asmatrix(UpperCamelCase      ).reshape(UpperCamelCase      ,							UpperCamelCase      )
       featuremap_pooled.append(UpperCamelCase      )
     return featuremap_pooled







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     # expanding three dimension data to one dimension list
     __a			=					[]
     for i in range(len(UpperCamelCase      )      ):
       __a			=					np.shape(data[i]      )
       __a			=					data[i].reshape(1      ,							shapes[0] * shapes[1]      )
       __a			=					data_listed.getA().tolist()[0]
       data_expanded.extend(UpperCamelCase      )
     __a			=					np.asarray(UpperCamelCase      )
     return data_expanded







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict:
     # expanding matrix to one dimension list
     __a			=					np.asarray(UpperCamelCase      )
     __a			=					np.shape(UpperCamelCase      )
     __a			=					data_mat.reshape(1      ,							shapes[0] * shapes[1]      )
     return data_expanded







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[str]:
     __a			=					[]
     __a			=					0
     for i_map in range(UpperCamelCase      ):
       __a			=					np.ones((size_map, size_map)      )
       for i in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
         for j in range(0      ,							UpperCamelCase      ,							UpperCamelCase      ):
           __a			=					pd_pool[
               i_pool
           ]
           __a			=					i_pool + 1
       __a			=					np.multiply(
           UpperCamelCase      ,							np.multiply(out_map[i_map]      ,							(1 - out_map[i_map])      )      )
       pd_all.append(UpperCamelCase      )
     return pd_all







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

         __a			=					np.dot(UpperCamelCase      ,							self.vji.T      ) - self.thre_bpa
         __a			=					self.sig(UpperCamelCase      )
         __a			=					np.dot(UpperCamelCase      ,							self.wkj.T      ) - self.thre_bpa
         __a			=					self.sig(UpperCamelCase      )

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

         __a			=					pd_i_all / (self.size_poolinga * self.size_poolinga)
         __a			=					pd_conva_pooled.T.getA().tolist()
         __a			=					self._calculate_gradient_from_pool(
             UpperCamelCase      ,							UpperCamelCase      ,							shape_featuremapa[0]      ,							shape_featuremapa[1]      ,							self.size_poolinga      ,							)
         # weight and threshold learning process---------
         # convolution layer
         for k_conv in range(self.conva[1]      ):
           __a			=					self._expand_mat(pd_conva_all[k_conv]      )
           __a			=					self.rate_weight * np.dot(UpperCamelCase      ,							UpperCamelCase      )

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

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

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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
     # return the data of image after convoluting process so we can check it out
     __a			=					np.asmatrix(UpperCamelCase      )
     __a	,       __a			=					self.convolute(
         UpperCamelCase      ,							self.conva      ,							self.w_conva      ,							self.thre_conva      ,							conv_step=self.step_conva      ,							)
     __a			=					self.pooling(UpperCamelCase      ,							self.size_poolinga      )

     return data_conveda, data_pooleda


if __name__ == "__main__":
  pass









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """distilbert"""
   _a							=      {
       """hidden_size""": """dim""",
       """num_attention_heads""": """n_heads""",
       """num_hidden_layers""": """n_layers""",
   }







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=512      ,							UpperCamelCase=False      ,							UpperCamelCase=6      ,							UpperCamelCase=12      ,							UpperCamelCase=768      ,							UpperCamelCase=4 * 768      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.02      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0      ,							**UpperCamelCase      ,							)				->    int:
     __a			=					vocab_size
     __a			=					max_position_embeddings
     __a			=					sinusoidal_pos_embds
     __a			=					n_layers
     __a			=					n_heads
     __a			=					dim
     __a			=					hidden_dim
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation
     __a			=					initializer_range
     __a			=					qa_dropout
     __a			=					seq_classif_dropout
     super().__init__(**UpperCamelCase      ,							pad_token_id=UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):







   @property
   def 		UpperCamelCase__			(	self      )				->    Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
       __a			=					{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
       __a			=					{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
         ]      )



