






import pickle

import numpy as np
from matplotlib import pyplot as plt



class   lowerCamelCase				:







		'''simple docstring'''



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Any:
							# save model dict with pickle
							UpperCAmelCase_   :				List[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(_UpperCamelCase    ,		'wb'       ) as f:
												pickle.dump(_UpperCamelCase    ,		_UpperCamelCase       )

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



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase       )     ->							List[str]:
							# read saved model
							with open(_UpperCamelCase    ,		'rb'       ) as f:
												UpperCAmelCase_   :				Optional[int]						=						pickle.load(_UpperCamelCase       )  # noqa: S301

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



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[str]:
							return round(_UpperCamelCase    ,		3       )



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

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



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



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



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



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



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

																	UpperCAmelCase_   :				str						=						np.dot(_UpperCamelCase    ,		self.vji.T       ) - self.thre_bpa
																	UpperCAmelCase_   :				Dict						=						self.sig(_UpperCamelCase       )
																	UpperCAmelCase_   :				Union[str, Any]						=						np.dot(_UpperCamelCase    ,		self.wkj.T       ) - self.thre_bpa
																	UpperCAmelCase_   :				int						=						self.sig(_UpperCamelCase       )

																	# --------------Model Leaning ------------------------
																	# calculate error and gradient---------------
																	UpperCAmelCase_   :				Dict						=						np.multiply(
																	    (data_teach - bp_outa)    ,		np.multiply(_UpperCamelCase    ,		(1 - bp_outa)       )       )
																	UpperCAmelCase_   :				str						=						np.multiply(
																	    np.dot(_UpperCamelCase    ,		self.wkj       )    ,		np.multiply(_UpperCamelCase    ,		(1 - bp_outa)       )       )
																	UpperCAmelCase_   :				Dict						=						np.dot(_UpperCamelCase    ,		self.vji       )

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

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

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

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

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



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

							return data_conveda, data_pooleda


if __name__ == "__main__":
		pass














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__UpperCAmelCase     							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






