





'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class    UpperCAmelCase	:
 def __init__(				self      :Optional[Any]						,       lowercase_      :Union[str, Any]						,       lowercase_      :Dict						,       lowercase_      :Optional[int]						,       lowercase_      :Optional[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :int=0.2						,       lowercase_      :Any=0.2							)->							Optional[Any]:
     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      :List[str]						,       lowercase_      :Any							)->							Tuple:
     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(snake_case__						,       "wb"							) as f:
         pickle.dump(snake_case__						,       snake_case__							)

     print(F"Model saved： {save_path}"							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       lowercase_      :str							)->							Union[str, Any]:


     with open(snake_case__						,       "rb"							) as f:
         A__				=			pickle.load(snake_case__							)  # 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(snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__							)
     # 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      :str						,       lowercase_      :int							)->							List[str]:
     return 1 / (1 + np.exp(-1 * x							))
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return round(snake_case__						,       3							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :List[str]						,       lowercase_      :Tuple						,       lowercase_      :List[Any]						,       lowercase_      :int						,       lowercase_      :Optional[int]							)->							Any:
     A__				=			convs[0]
     A__				=			convs[1]
     A__				=			np.shape(snake_case__							)[0]
     # get the data slice of original image data, data_focus
     A__				=			[]
     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__							):
             A__				=			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
     A__				=			[]
     A__				=			int((size_data - size_conv) / conv_step + 1							)
     for i_map in range(snake_case__							):
         A__				=			[]
         for i_focus in range(len(snake_case__							)							):
             A__				=			(
                 np.sum(np.multiply(data_focus[i_focus]						,       w_convs[i_map]							)							)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(snake_case__							)							)
         A__				=			np.asmatrix(snake_case__							).reshape(
             snake_case__						,       snake_case__							)
         data_featuremap.append(snake_case__							)

     # expanding the data slice to One dimenssion
     A__				=			[]
     for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(snake_case__							)							)
     A__				=			np.asarray(snake_case__							)
     return focus_list, data_featuremap
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :Dict						,       lowercase_      :List[str]						,       lowercase_      :List[str]="average_pool"							)->							List[str]:
     A__				=			len(featuremaps[0]							)
     A__				=			int(size_map / size_pooling							)
     A__				=			[]
     for i_map in range(len(snake_case__							)							):
         A__				=			featuremaps[i_map]
         A__				=			[]
         for i_focus in range(0						,       snake_case__						,       snake_case__							):
             for j_focus in range(0						,       snake_case__						,       snake_case__							):
                 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(snake_case__							)							)
                 elif pooling_type == "max_pooling":
                     # max pooling
                     map_pooled.append(np.max(snake_case__							)							)
         A__				=			np.asmatrix(snake_case__							).reshape(snake_case__						,       snake_case__							)
         featuremap_pooled.append(snake_case__							)
     return featuremap_pooled
 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]							)->							List[str]:
     A__				=			[]
     for i in range(len(snake_case__							)							):
         A__				=			np.shape(data[i]							)
         A__				=			data[i].reshape(1						,       shapes[0] * shapes[1]							)
         A__				=			data_listed.getA().tolist()[0]
         data_expanded.extend(snake_case__							)
     A__				=			np.asarray(snake_case__							)
     return data_expanded
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[str]							)->							Optional[int]:
     A__				=			np.asarray(snake_case__							)
     A__				=			np.shape(snake_case__							)
     A__				=			data_mat.reshape(1						,       shapes[0] * shapes[1]							)
     return data_expanded
 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :Optional[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :List[Any]						,       lowercase_      :Union[str, Any]						,       lowercase_      :Optional[int]							)->							Tuple:
     A__				=			[]
     A__				=			0
     for i_map in range(snake_case__							):
         A__				=			np.ones((size_map, size_map)							)
         for i in range(0						,       snake_case__						,       snake_case__							):
             for j in range(0						,       snake_case__						,       snake_case__							):
                 A__				=			pd_pool[
                     i_pool
                 ]
                 A__				=			i_pool + 1
         A__				=			np.multiply(
             snake_case__						,       np.multiply(out_map[i_map]						,       (1 - out_map[i_map])							)							)
         pd_all.append(snake_case__							)
     return pd_all
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :Optional[int]						,       lowercase_      :Optional[int]						,       lowercase_      :List[Any]						,       lowercase_      :Any						,       lowercase_      :Optional[int]						,       lowercase_      :Any=bool							)->							str:
     print("----------------------Start Training-------------------------"							)
     print((" - - Shape: Train_Data  ", np.shape(snake_case__							))							)
     print((" - - Shape: Teach_Data  ", np.shape(snake_case__							))							)
     A__				=			0
     A__				=			[]
     A__				=			1_00_00
     while rp < n_repeat and mse >= error_accuracy:
         A__				=			0
         print(F"-------------Learning Time {rp}--------------"							)
         for p in range(len(snake_case__							)							):
             # print('------------Learning Image: %d--------------'%p)
             A__				=			np.asmatrix(datas_train[p]							)
             A__				=			np.asarray(datas_teach[p]							)
             A__				=			self.convolute(
                 snake_case__						,       self.conva						,       self.w_conva						,       self.thre_conva						,       conv_step=self.step_conva						,       )
             A__				=			self.pooling(snake_case__						,       self.size_poolinga							)
             A__				=			np.shape(snake_case__							)
             A__				=			self._expand(snake_case__							)
             A__				=			data_bp_input

             A__				=			np.dot(snake_case__						,       self.vji.T							) - self.thre_bpa
             A__				=			self.sig(snake_case__							)
             A__				=			np.dot(snake_case__						,       self.wkj.T							) - self.thre_bpa
             A__				=			self.sig(snake_case__							)

             # --------------Model Leaning ------------------------
             # calculate error and gradient---------------
             A__				=			np.multiply(
                 (data_teach - bp_outa)						,       np.multiply(snake_case__						,       (1 - bp_outa)							)							)
             A__				=			np.multiply(
                 np.dot(snake_case__						,       self.wkj							)						,       np.multiply(snake_case__						,       (1 - bp_outa)							)							)
             A__				=			np.dot(snake_case__						,       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(
                 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]							):
                 A__				=			self._expand_mat(pd_conva_all[k_conv]							)
                 A__				=			self.rate_weight * np.dot(snake_case__						,       snake_case__							)

                 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(snake_case__							)

     def draw_error():
         A__				=			[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      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Optional[Any]							)->							Optional[int]:
     A__				=			[]
     print("-------------------Start Testing-------------------------"							)
     print((" - - Shape: Test_Data  ", np.shape(snake_case__							))							)
     for p in range(len(snake_case__							)							):
         A__				=			np.asmatrix(datas_test[p]							)
         A__				=			self.convolute(
             snake_case__						,       self.conva						,       self.w_conva						,       self.thre_conva						,       conv_step=self.step_conva						,       )
         A__				=			self.pooling(snake_case__						,       self.size_poolinga							)
         A__				=			self._expand(snake_case__							)

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



 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Dict							)->							Dict:
     A__				=			np.asmatrix(snake_case__							)
     A__				=			self.convolute(
         snake_case__						,       self.conva						,       self.w_conva						,       self.thre_conva						,       conv_step=self.step_conva						,       )
     A__				=			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    )





