


import pickle

import numpy as np
from matplotlib import pyplot as plt






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=0.2, lowerCamelCase=0.2)			->  str:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				List[str] 						=						{
               '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      UpperCamelCase	(   cls, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           with open(lowerCamelCase, 'rb') as f:
               _lowercase			:				Union[str, Any] 						=						pickle.load(lowerCamelCase)  # noqa: S301

           _lowercase			:				int 						=						model_dic.get('conv1')
           conv_get.append(model_dic.get('step_conv1'))
           _lowercase			:				Optional[int] 						=						model_dic.get('size_pooling1')
           _lowercase			:				Union[str, Any] 						=						model_dic.get('num_bp1')
           _lowercase			:				Union[str, Any] 						=						model_dic.get('num_bp2')
           _lowercase			:				Tuple 						=						model_dic.get('num_bp3')
           _lowercase			:				int 						=						model_dic.get('rate_weight')
           _lowercase			:				List[str] 						=						model_dic.get('rate_thre')
           # create model instance
           _lowercase			:				List[Any] 						=						CNN(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)
           # modify model parameter
           _lowercase			:				Optional[Any] 						=						model_dic.get('w_conv1')
           _lowercase			:				int 						=						model_dic.get('wkj')
           _lowercase			:				Dict 						=						model_dic.get('vji')
           _lowercase			:				Optional[Any] 						=						model_dic.get('thre_conv1')
           _lowercase			:				List[Any] 						=						model_dic.get('thre_bp2')
           _lowercase			:				Any 						=						model_dic.get('thre_bp3')
           return conv_ins
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

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




           """simple docstring"""

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




           """simple docstring"""

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

           # expanding the data slice to One dimenssion
           _lowercase			:				str 						=						[]
           for each_focus in data_focus:
               focusa_list.extend(self.Expand_Mat(lowerCamelCase))
           _lowercase			:				Any 						=						np.asarray(lowerCamelCase)
           return focus_list, data_featuremap
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase="average_pool")			->  Any:




           """simple docstring"""

           _lowercase			:				Tuple 						=						len(featuremaps[0])
           _lowercase			:				List[Any] 						=						int(size_map / size_pooling)
           _lowercase			:				Optional[int] 						=						[]
           for i_map in range(len(lowerCamelCase)):
               _lowercase			:				Union[str, Any] 						=						featuremaps[i_map]
               _lowercase			:				Union[str, Any] 						=						[]
               for i_focus in range(0, lowerCamelCase, lowerCamelCase):
                   for j_focus in range(0, lowerCamelCase, lowerCamelCase):
                       _lowercase			:				List[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))
               _lowercase			:				Tuple 						=						np.asmatrix(lowerCamelCase).reshape(lowerCamelCase, lowerCamelCase)
               featuremap_pooled.append(lowerCamelCase)
           return featuremap_pooled
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						[]
           for i in range(len(lowerCamelCase)):
               _lowercase			:				Optional[int] 						=						np.shape(data[i])
               _lowercase			:				Optional[Any] 						=						data[i].reshape(1, shapes[0] * shapes[1])
               _lowercase			:				int 						=						data_listed.getA().tolist()[0]
               data_expanded.extend(lowerCamelCase)
           _lowercase			:				Tuple 						=						np.asarray(lowerCamelCase)
           return data_expanded
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						np.asarray(lowerCamelCase)
           _lowercase			:				Optional[int] 						=						np.shape(lowerCamelCase)
           _lowercase			:				Any 						=						data_mat.reshape(1, shapes[0] * shapes[1])
           return data_expanded
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						[]
           _lowercase			:				Dict 						=						0
           for i_map in range(lowerCamelCase):
               _lowercase			:				Tuple 						=						np.ones((size_map, size_map))
               for i in range(0, lowerCamelCase, lowerCamelCase):
                   for j in range(0, lowerCamelCase, lowerCamelCase):
                       _lowercase			:				Optional[int] 						=						pd_pool[
                           i_pool
                       ]
                       _lowercase			:				Dict 						=						i_pool + 1
               _lowercase			:				List[Any] 						=						np.multiply(
                   lowerCamelCase, np.multiply(out_map[i_map], (1 - out_map[i_map])))
               pd_all.append(lowerCamelCase)
           return pd_all
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=bool)			->  List[str]:




           """simple docstring"""

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

                   _lowercase			:				Dict 						=						np.dot(lowerCamelCase, self.vji.T) - self.thre_bpa
                   _lowercase			:				Dict 						=						self.sig(lowerCamelCase)
                   _lowercase			:				int 						=						np.dot(lowerCamelCase, self.wkj.T) - self.thre_bpa
                   _lowercase			:				Tuple 						=						self.sig(lowerCamelCase)

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

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

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

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

           def draw_error():
               _lowercase			:				List[Any] 						=						[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      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

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

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




       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						np.asmatrix(lowerCamelCase)
           _lowercase						,							_lowercase			:				Optional[Any] 						=						self.convolute(
               lowerCamelCase, self.conva, self.w_conva, self.thre_conva, conv_step=self.step_conva, )
           _lowercase			:				Optional[Any] 						=						self.pooling(lowerCamelCase, self.size_poolinga)

           return data_conveda, data_pooleda


if __name__ == "__main__":
 pass






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=2		,      A=0.02		,      A=1e-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__(pad_token_id=A		,      **A	)

					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  position_embedding_type
					lowerCamelCase    		=  classifier_dropout
					lowerCamelCase    		=  channel_shrink_ratio
					lowerCamelCase    		=  max_ad_position_embeddings

