
import pickle

import numpy as np
from matplotlib import pyplot as plt






class   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					int=0.2    ,						snake_case__		:					Any=0.2 )		->	Optional[Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Any )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      {
              "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(snake_case__    ,						"wb" ) as f:
                 pickle.dump(snake_case__    ,						snake_case__ )

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



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Dict    ,						snake_case__		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          with open(snake_case__    ,						"rb" ) as f:
                 snake_case       :   int							=      pickle.load(snake_case__ )  # noqa: S301

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					int )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          return round(snake_case__    ,						3 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					List[str]    ,						snake_case__		:					Tuple    ,						snake_case__		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Dict    ,						snake_case__		:					List[str]    ,						snake_case__		:					List[str]="average_pool" )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      np.asarray(snake_case__ )
          snake_case       :   Tuple							=      np.shape(snake_case__ )
          snake_case       :   Union[str, Any]							=      data_mat.reshape(1    ,						shapes[0] * shapes[1] )
          return data_expanded



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Optional[int] )		->	Tuple:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Any=bool )		->	str:


          '''simple docstring'''


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

                        snake_case       :   str							=      np.dot(snake_case__    ,						self.vji.T ) - self.thre_bpa
                        snake_case       :   int							=      self.sig(snake_case__ )
                        snake_case       :   Tuple							=      np.dot(snake_case__    ,						self.wkj.T ) - self.thre_bpa
                        snake_case       :   Any							=      self.sig(snake_case__ )

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

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

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

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

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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


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

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					Dict )		->	Dict:


          '''simple docstring'''


          snake_case       :   List[Any]							=      np.asmatrix(snake_case__ )
          snake_case				,					snake_case       :   Tuple							=      self.convolute(
              snake_case__    ,						self.conva    ,						self.w_conva    ,						self.thre_conva    ,						conv_step=self.step_conva    ,						)
          snake_case       :   str							=      self.pooling(snake_case__    ,						self.size_poolinga )

          return data_conveda, data_pooleda


if __name__ == "__main__":
   pass







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


                 return self._metrics



       monkeypatch.setattr("datasets.inspect.huggingface_hub"				,					HfhMock()    )




@pytest.mark.parametrize(
    "func, args"				,					[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]    )
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       if "tmp_path" in args:
              snake_case       :   str							=      tuple(arg if arg != "tmp_path" else tmp_path for arg in args    )
       with pytest.warns(__lowerCamelCase				,					match="https://huggingface.co/docs/evaluate"    ):
              func(*__lowerCamelCase    )





