




import pickle

import numpy as np
from matplotlib import pyplot as plt




class       a__	:







     def __init__(     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A=0.2  ,		A=0.2			)  ->   Any:



          '''simple docstring'''



          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       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          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(A  ,		"wb"			) as f:
               pickle.dump(A  ,		A			)

          print(F'''Model saved： {save_path}'''			)







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          with open(A  ,		"rb"			) as f:
               a     =				pickle.load(A			)  # 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(A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)
          # 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       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          return round(A  ,		3			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



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

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A="average_pool"			)  ->   Tuple:



          '''simple docstring'''



          a     =				len(featuremaps[0]			)
          a     =				int(size_map / size_pooling			)
          a     =				[]
          for i_map in range(len(A			)			):
               a     =				featuremaps[i_map]
               a     =				[]
               for i_focus in range(0  ,		A  ,		A			):
                    for j_focus in range(0  ,		A  ,		A			):
                         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(A			)			)
                         elif pooling_type == "max_pooling":
                              # max pooling
                              map_pooled.append(np.max(A			)			)
               a     =				np.asmatrix(A			).reshape(A  ,		A			)
               featuremap_pooled.append(A			)
          return featuremap_pooled







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				[]
          for i in range(len(A			)			):
               a     =				np.shape(data[i]			)
               a     =				data[i].reshape(1  ,		shapes[0] * shapes[1]			)
               a     =				data_listed.getA().tolist()[0]
               data_expanded.extend(A			)
          a     =				np.asarray(A			)
          return data_expanded







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				np.asarray(A			)
          a     =				np.shape(A			)
          a     =				data_mat.reshape(1  ,		shapes[0] * shapes[1]			)
          return data_expanded







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Tuple:



          '''simple docstring'''



          a     =				[]
          a     =				0
          for i_map in range(A			):
               a     =				np.ones((size_map, size_map)			)
               for i in range(0  ,		A  ,		A			):
                    for j in range(0  ,		A  ,		A			):
                         a     =				pd_pool[
                             i_pool
                         ]
                         a     =				i_pool + 1
               a     =				np.multiply(
                   A  ,		np.multiply(out_map[i_map]  ,		(1 - out_map[i_map])			)			)
               pd_all.append(A			)
          return pd_all







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A=bool			)  ->   str:



          '''simple docstring'''



          print("----------------------Start Training-------------------------"			)
          print((" - - Shape: Train_Data  ", np.shape(A			))			)
          print((" - - Shape: Teach_Data  ", np.shape(A			))			)
          a     =				0
          a     =				[]
          a     =				10000
          while rp < n_repeat and mse >= error_accuracy:
               a     =				0
               print(F'''-------------Learning Time {rp}--------------'''			)
               for p in range(len(A			)			):
                    # print('------------Learning Image: %d--------------'%p)
                    a     =				np.asmatrix(datas_train[p]			)
                    a     =				np.asarray(datas_teach[p]			)
                    a  ,				a     =				self.convolute(
                        A  ,		self.conva  ,		self.w_conva  ,		self.thre_conva  ,		conv_step=self.step_conva  ,		)
                    a     =				self.pooling(A  ,		self.size_poolinga			)
                    a     =				np.shape(A			)
                    a     =				self._expand(A			)
                    a     =				data_bp_input

                    a     =				np.dot(A  ,		self.vji.T			) - self.thre_bpa
                    a     =				self.sig(A			)
                    a     =				np.dot(A  ,		self.wkj.T			) - self.thre_bpa
                    a     =				self.sig(A			)

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

                         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(A			)

          def draw_error():
               a     =				[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       lowerCAmelCase_    (     self  ,		A			)  ->   int:



          '''simple docstring'''



          a     =				[]
          print("-------------------Start Testing-------------------------"			)
          print((" - - Shape: Test_Data  ", np.shape(A			))			)
          for p in range(len(A			)			):
               a     =				np.asmatrix(datas_test[p]			)
               a  ,				a     =				self.convolute(
                   A  ,		self.conva  ,		self.w_conva  ,		self.thre_conva  ,		conv_step=self.step_conva  ,		)
               a     =				self.pooling(A  ,		self.size_poolinga			)
               a     =				self._expand(A			)

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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				np.asmatrix(A			)
          a  ,				a     =				self.convolute(
              A  ,		self.conva  ,		self.w_conva  ,		self.thre_conva  ,		conv_step=self.step_conva  ,		)
          a     =				self.pooling(A  ,		self.size_poolinga			)

          return data_conveda, data_pooleda


if __name__ == "__main__":
   pass







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

