"""simple docstring"""





import pickle

import numpy as np
from matplotlib import pyplot as plt


class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a		,			a		,			a		,			a		,			a=0.2		,			a=0.2							)       ->					Dict:
             lowercase__			:		Any       =	bp_numa
             lowercase__			:		Optional[int]       =	bp_numa
             lowercase__			:		Tuple       =	bp_numa
             lowercase__			:		Optional[Any]       =	conva_get[:2]
             lowercase__			:		Optional[int]       =	conva_get[2]
             lowercase__			:		Optional[Any]       =	size_pa
             lowercase__			:		Union[str, Any]       =	rate_w
             lowercase__			:		Union[str, Any]       =	rate_t
             lowercase__			:		List[Any]       =	[
                 np.mat(-1 * np.random.rand(self.conva[0]		,			self.conva[0]							) + 0.5							)
                 for i in range(self.conva[1]							)
             ]
             lowercase__			:		Optional[Any]       =	np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa							) + 0.5							)
             lowercase__			:		int       =	np.mat(-1 * np.random.rand(self.num_bpa		,			self.num_bpa							) + 0.5							)
             lowercase__			:		Any       =	-2 * np.random.rand(self.conva[1]							) + 1
             lowercase__			:		int       =	-2 * np.random.rand(self.num_bpa							) + 1
             lowercase__			:		int       =	-2 * np.random.rand(self.num_bpa							) + 1


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Union[str, Any]:
             # save model dict with pickle
             lowercase__			:		Optional[Any]       =	{
                 '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(a		,			'wb'							) as f:
                    pickle.dump(a		,			a							)

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


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

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


      def 		_UpperCAmelCase       (   self		,			a							)       ->					str:
             return 1 / (1 + np.exp(-1 * x							))


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Any:
             return round(a		,			3							)


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

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


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


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


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict:
             # expanding matrix to one dimension list
             lowercase__			:		Dict       =	np.asarray(a							)
             lowercase__			:		Union[str, Any]       =	np.shape(a							)
             lowercase__			:		Optional[Any]       =	data_mat.reshape(1		,			shapes[0] * shapes[1]							)
             return data_expanded


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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a		,			a=bool							)       ->					str:
             # model traning
             print('----------------------Start Training-------------------------'							)
             print((' - - Shape: Train_Data  ', np.shape(a							))							)
             print((' - - Shape: Teach_Data  ', np.shape(a							))							)
             lowercase__			:		int       =	0
             lowercase__			:		List[Any]       =	[]
             lowercase__			:		Union[str, Any]       =	1_0_0_0_0
             while rp < n_repeat and mse >= error_accuracy:
                    lowercase__			:		List[Any]       =	0
                    print(f"""-------------Learning Time {rp}--------------"""							)
                    for p in range(len(a							)							):
                           # print('------------Learning Image: %d--------------'%p)
                           lowercase__			:		Optional[int]       =	np.asmatrix(datas_train[p]							)
                           lowercase__			:		int       =	np.asarray(datas_teach[p]							)
                           lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.convolute(
                               a		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
                           lowercase__			:		Optional[Any]       =	self.pooling(a		,			self.size_poolinga							)
                           lowercase__			:		Tuple       =	np.shape(a							)
                           lowercase__			:		List[str]       =	self._expand(a							)
                           lowercase__			:		Optional[int]       =	data_bp_input

                           lowercase__			:		Optional[Any]       =	np.dot(a		,			self.vji.T							) - self.thre_bpa
                           lowercase__			:		str       =	self.sig(a							)
                           lowercase__			:		Tuple       =	np.dot(a		,			self.wkj.T							) - self.thre_bpa
                           lowercase__			:		Any       =	self.sig(a							)

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

                           lowercase__			:		Union[str, Any]       =	pd_i_all / (self.size_poolinga * self.size_poolinga)
                           lowercase__			:		Any       =	pd_conva_pooled.T.getA().tolist()
                           lowercase__			:		List[str]       =	self._calculate_gradient_from_pool(
                               a		,			a		,			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__			:		Optional[int]       =	self._expand_mat(pd_conva_all[k_conv]							)
                                  lowercase__			:		Tuple       =	self.rate_weight * np.dot(a		,			a							)

                                  lowercase__			:		Union[str, Any]       =	self.w_conva[k_conv] + delta_w.reshape(
                                      (self.conva[0], self.conva[0])							)

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

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

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


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[str]:
             # return the data of image after convoluting process so we can check it out
             lowercase__			:		Any       =	np.asmatrix(a							)
             lowercase__    ,      lowercase__			:		str       =	self.convolute(
                 a		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
             lowercase__			:		Tuple       =	self.pooling(a		,			self.size_poolinga							)

             return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



