


"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=0.2 ,						__lowerCamelCase=0.2  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						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  UpperCamelCase__(   cls ,						__lowerCamelCase  ):



     '''simple docstring'''




     with open(__lowerCamelCase ,						'''rb'''  ) as f:
         __A   :						int									=					pickle.load(__lowerCamelCase  )  # noqa: S301

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return round(__lowerCamelCase ,						3  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase="average_pool"  ):



     '''simple docstring'''




     __A   :						Tuple									=					len(featuremaps[0]  )
     __A   :						Any									=					int(size_map / size_pooling  )
     __A   :						Optional[int]									=					[]
     for i_map in range(len(__lowerCamelCase  )  ):
         __A   :						Tuple									=					featuremaps[i_map]
         __A   :						int									=					[]
         for i_focus in range(0 ,						__lowerCamelCase ,						__lowerCamelCase  ):
             for j_focus in range(0 ,						__lowerCamelCase ,						__lowerCamelCase  ):
                 __A   :						int									=					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  )  )
         __A   :						Tuple									=					np.asmatrix(__lowerCamelCase  ).reshape(__lowerCamelCase ,						__lowerCamelCase  )
         featuremap_pooled.append(__lowerCamelCase  )
     return featuremap_pooled

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					np.asarray(__lowerCamelCase  )
     __A   :						Optional[Any]									=					np.shape(__lowerCamelCase  )
     __A   :						Any									=					data_mat.reshape(1 ,						shapes[0] * shapes[1]  )
     return data_expanded

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					[]
     __A   :						Optional[int]									=					0
     for i_map in range(__lowerCamelCase  ):
         __A   :						Any									=					np.ones((size_map, size_map)  )
         for i in range(0 ,						__lowerCamelCase ,						__lowerCamelCase  ):
             for j in range(0 ,						__lowerCamelCase ,						__lowerCamelCase  ):
                 __A   :						int									=					pd_pool[
                     i_pool
                 ]
                 __A   :						Optional[Any]									=					i_pool + 1
         __A   :						Tuple									=					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  ):



     '''simple docstring'''




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

             __A   :						Union[str, Any]									=					np.dot(__lowerCamelCase ,						self.vji.T  ) - self.thre_bpa
             __A   :						Optional[int]									=					self.sig(__lowerCamelCase  )
             __A   :						Dict									=					np.dot(__lowerCamelCase ,						self.wkj.T  ) - self.thre_bpa
             __A   :						List[Any]									=					self.sig(__lowerCamelCase  )

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

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

                 __A   :						Optional[int]									=					self.w_conva[k_conv] + delta_w.reshape(
                     (self.conva[0], self.conva[0])  )

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

     def draw_error():
         __A   :						str									=					[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  ):



     '''simple docstring'''




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

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







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Tuple									=					np.asmatrix(__lowerCamelCase  )
     __A   :						int									=					self.convolute(
         __lowerCamelCase ,						self.conva ,						self.w_conva ,						self.thre_conva ,						conv_step=self.step_conva ,						)
     __A   :						str									=					self.pooling(__lowerCamelCase ,						self.size_poolinga  )

     return data_conveda, data_pooleda


if __name__ == "__main__":
       pass











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




