

import pickle

import numpy as np
from matplotlib import pyplot as plt







class 					lowercase							:




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case=0.2 ,  snake_case=0.2			):
											snake_case_     							=       bp_numa
											snake_case_     							=       bp_numa
											snake_case_     							=       bp_numa
											snake_case_     							=       conva_get[:2]
											snake_case_     							=       conva_get[2]
											snake_case_     							=       size_pa
											snake_case_     							=       rate_w
											snake_case_     							=       rate_t
											snake_case_     							=       [
											    np.mat(-1 * np.random.rand(self.conva[0] ,  self.conva[0]			) + 0.5			)
											    for i in range(self.conva[1]			)
											]
											snake_case_     							=       np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa			) + 0.5			)
											snake_case_     							=       np.mat(-1 * np.random.rand(self.num_bpa ,  self.num_bpa			) + 0.5			)
											snake_case_     							=       -2 * np.random.rand(self.conva[1]			) + 1
											snake_case_     							=       -2 * np.random.rand(self.num_bpa			) + 1
											snake_case_     							=       -2 * np.random.rand(self.num_bpa			) + 1




				def        a				(   self ,  snake_case			):
											# save model dict with pickle
											snake_case_     							=       {
											    '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        a				(   cls ,  snake_case			):
											# read saved model
											with open(snake_case ,  'rb'			) as f:
																		snake_case_     							=       pickle.load(snake_case			)  # noqa: S301

											snake_case_     							=       model_dic.get('conv1'			)
											conv_get.append(model_dic.get('step_conv1'			)			)
											snake_case_     							=       model_dic.get('size_pooling1'			)
											snake_case_     							=       model_dic.get('num_bp1'			)
											snake_case_     							=       model_dic.get('num_bp2'			)
											snake_case_     							=       model_dic.get('num_bp3'			)
											snake_case_     							=       model_dic.get('rate_weight'			)
											snake_case_     							=       model_dic.get('rate_thre'			)
											# create model instance
											snake_case_     							=       CNN(snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case			)
											# modify model parameter
											snake_case_     							=       model_dic.get('w_conv1'			)
											snake_case_     							=       model_dic.get('wkj'			)
											snake_case_     							=       model_dic.get('vji'			)
											snake_case_     							=       model_dic.get('thre_conv1'			)
											snake_case_     							=       model_dic.get('thre_bp2'			)
											snake_case_     							=       model_dic.get('thre_bp3'			)
											return conv_ins




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




				def        a				(   self ,  snake_case			):
											return round(snake_case ,  3			)




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

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




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case="average_pool"			):
											# pooling process
											snake_case_     							=       len(featuremaps[0]			)
											snake_case_     							=       int(size_map / size_pooling			)
											snake_case_     							=       []
											for i_map in range(len(snake_case			)			):
																		snake_case_     							=       featuremaps[i_map]
																		snake_case_     							=       []
																		for i_focus in range(0 ,  snake_case ,  snake_case			):
																									for j_focus in range(0 ,  snake_case ,  snake_case			):
																																snake_case_     							=       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_     							=       np.asmatrix(snake_case			).reshape(snake_case ,  snake_case			)
																		featuremap_pooled.append(snake_case			)
											return featuremap_pooled




				def        a				(   self ,  snake_case			):
											# expanding three dimension data to one dimension list
											snake_case_     							=       []
											for i in range(len(snake_case			)			):
																		snake_case_     							=       np.shape(data[i]			)
																		snake_case_     							=       data[i].reshape(1 ,  shapes[0] * shapes[1]			)
																		snake_case_     							=       data_listed.getA().tolist()[0]
																		data_expanded.extend(snake_case			)
											snake_case_     							=       np.asarray(snake_case			)
											return data_expanded




				def        a				(   self ,  snake_case			):
											# expanding matrix to one dimension list
											snake_case_     							=       np.asarray(snake_case			)
											snake_case_     							=       np.shape(snake_case			)
											snake_case_     							=       data_mat.reshape(1 ,  shapes[0] * shapes[1]			)
											return data_expanded




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       []
											snake_case_     							=       0
											for i_map in range(snake_case			):
																		snake_case_     							=       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_     							=       pd_pool[
																																    i_pool
																																]
																																snake_case_     							=       i_pool + 1
																		snake_case_     							=       np.multiply(
																		    snake_case ,  np.multiply(out_map[i_map] ,  (1 - out_map[i_map])			)			)
																		pd_all.append(snake_case			)
											return pd_all




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case=bool			):
											# model traning
											print('----------------------Start Training-------------------------'			)
											print((' - - Shape: Train_Data  ', np.shape(snake_case			))			)
											print((' - - Shape: Teach_Data  ', np.shape(snake_case			))			)
											snake_case_     							=       0
											snake_case_     							=       []
											snake_case_     							=       1_0000
											while rp < n_repeat and mse >= error_accuracy:
																		snake_case_     							=       0
																		print(F'''-------------Learning Time {rp}--------------'''			)
																		for p in range(len(snake_case			)			):
																									# print('------------Learning Image: %d--------------'%p)
																									snake_case_     							=       np.asmatrix(datas_train[p]			)
																									snake_case_     							=       np.asarray(datas_teach[p]			)
																									snake_case_							,       snake_case_     							=       self.convolute(
																									    snake_case ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
																									snake_case_     							=       self.pooling(snake_case ,  self.size_poolinga			)
																									snake_case_     							=       np.shape(snake_case			)
																									snake_case_     							=       self._expand(snake_case			)
																									snake_case_     							=       data_bp_input

																									snake_case_     							=       np.dot(snake_case ,  self.vji.T			) - self.thre_bpa
																									snake_case_     							=       self.sig(snake_case			)
																									snake_case_     							=       np.dot(snake_case ,  self.wkj.T			) - self.thre_bpa
																									snake_case_     							=       self.sig(snake_case			)

																									# --------------Model Leaning ------------------------
																									# calculate error and gradient---------------
																									snake_case_     							=       np.multiply(
																									    (data_teach - bp_outa) ,  np.multiply(snake_case ,  (1 - bp_outa)			)			)
																									snake_case_     							=       np.multiply(
																									    np.dot(snake_case ,  self.wkj			) ,  np.multiply(snake_case ,  (1 - bp_outa)			)			)
																									snake_case_     							=       np.dot(snake_case ,  self.vji			)

																									snake_case_     							=       pd_i_all / (self.size_poolinga * self.size_poolinga)
																									snake_case_     							=       pd_conva_pooled.T.getA().tolist()
																									snake_case_     							=       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_     							=       self._expand_mat(pd_conva_all[k_conv]			)
																																snake_case_     							=       self.rate_weight * np.dot(snake_case ,  snake_case			)

																																snake_case_     							=       self.w_conva[k_conv] + delta_w.reshape(
																																    (self.conva[0], self.conva[0])			)

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

											def draw_error():
																		snake_case_     							=       [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        a				(   self ,  snake_case			):
											# model predict
											snake_case_     							=       []
											print('-------------------Start Testing-------------------------'			)
											print((' - - Shape: Test_Data  ', np.shape(snake_case			))			)
											for p in range(len(snake_case			)			):
																		snake_case_     							=       np.asmatrix(datas_test[p]			)
																		snake_case_							,       snake_case_     							=       self.convolute(
																		    snake_case ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
																		snake_case_     							=       self.pooling(snake_case ,  self.size_poolinga			)
																		snake_case_     							=       self._expand(snake_case			)

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




				def        a				(   self ,  snake_case			):
											# return the data of image after convoluting process so we can check it out
											snake_case_     							=       np.asmatrix(snake_case			)
											snake_case_							,       snake_case_     							=       self.convolute(
											    snake_case ,  self.conva ,  self.w_conva ,  self.thre_conva ,  conv_step=self.step_conva ,  )
											snake_case_     							=       self.pooling(snake_case ,  self.size_poolinga			)

											return data_conveda, data_pooleda


if __name__ == "__main__":
					pass







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,				UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




