


'''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							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  {
						    '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							):

						'''simple docstring'''





						with open(__a						,    'rb'							) as f:
											__a      :	List[Any]    =  pickle.load(__a							)  # noqa: S301

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




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						return round(__a						,    3							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





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

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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a="average_pool"							):

						'''simple docstring'''





						__a      :	List[Any]    =  len(featuremaps[0]							)
						__a      :	Dict    =  int(size_map / size_pooling							)
						__a      :	Optional[int]    =  []
						for i_map in range(len(__a							)							):
											__a      :	List[str]    =  featuremaps[i_map]
											__a      :	Dict    =  []
											for i_focus in range(0						,    __a						,    __a							):
																for j_focus in range(0						,    __a						,    __a							):
																					__a      :	Dict    =  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      :	Dict    =  np.asmatrix(__a							).reshape(__a						,    __a							)
											featuremap_pooled.append(__a							)
						return featuremap_pooled




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[Any]    =  []
						for i in range(len(__a							)							):
											__a      :	str    =  np.shape(data[i]							)
											__a      :	Optional[Any]    =  data[i].reshape(1						,    shapes[0] * shapes[1]							)
											__a      :	Optional[int]    =  data_listed.getA().tolist()[0]
											data_expanded.extend(__a							)
						__a      :	Any    =  np.asarray(__a							)
						return data_expanded




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  np.asarray(__a							)
						__a      :	str    =  np.shape(__a							)
						__a      :	Dict    =  data_mat.reshape(1						,    shapes[0] * shapes[1]							)
						return data_expanded




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	int    =  []
						__a      :	int    =  0
						for i_map in range(__a							):
											__a      :	Tuple    =  np.ones((size_map, size_map)							)
											for i in range(0						,    __a						,    __a							):
																for j in range(0						,    __a						,    __a							):
																					__a      :	List[Any]    =  pd_pool[
																					    i_pool
																					]
																					__a      :	Union[str, Any]    =  i_pool + 1
											__a      :	str    =  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							):

						'''simple docstring'''





						print('----------------------Start Training-------------------------'							)
						print((' - - Shape: Train_Data  ', np.shape(__a							))							)
						print((' - - Shape: Teach_Data  ', np.shape(__a							))							)
						__a      :	Union[str, Any]    =  0
						__a      :	Optional[int]    =  []
						__a      :	List[str]    =  1_0000
						while rp < n_repeat and mse >= error_accuracy:
											__a      :	Optional[int]    =  0
											print(f"""-------------Learning Time {rp}--------------"""							)
											for p in range(len(__a							)							):
																# print('------------Learning Image: %d--------------'%p)
																__a      :	Any    =  np.asmatrix(datas_train[p]							)
																__a      :	str    =  np.asarray(datas_teach[p]							)
																__a      :	Optional[Any]    =  self.convolute(
																    __a						,    self.conva						,    self.w_conva						,    self.thre_conva						,    conv_step=self.step_conva						,    )
																__a      :	Optional[int]    =  self.pooling(__a						,    self.size_poolinga							)
																__a      :	List[Any]    =  np.shape(__a							)
																__a      :	str    =  self._expand(__a							)
																__a      :	Optional[int]    =  data_bp_input

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

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

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

																					__a      :	List[Any]    =  self.w_conva[k_conv] + delta_w.reshape(
																					    (self.conva[0], self.conva[0])							)

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

						def draw_error():
											__a      :	Union[str, 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							):

						'''simple docstring'''





						__a      :	Any    =  []
						print('-------------------Start Testing-------------------------'							)
						print((' - - Shape: Test_Data  ', np.shape(__a							))							)
						for p in range(len(__a							)							):
											__a      :	Optional[int]    =  np.asmatrix(datas_test[p]							)
											__a      :	int    =  self.convolute(
											    __a						,    self.conva						,    self.w_conva						,    self.thre_conva						,    conv_step=self.step_conva						,    )
											__a      :	Optional[int]    =  self.pooling(__a						,    self.size_poolinga							)
											__a      :	List[str]    =  self._expand(__a							)

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





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Tuple    =  np.asmatrix(__a							)
						__a      :	int    =  self.convolute(
						    __a						,    self.conva						,    self.w_conva						,    self.thre_conva						,    conv_step=self.step_conva						,    )
						__a      :	Tuple    =  self.pooling(__a						,    self.size_poolinga							)

						return data_conveda, data_pooleda


if __name__ == "__main__":
	pass







'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase     :						Union[str, Any]	=     {
    '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:
	__lowercase     :						List[str]	=     [
	    '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

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



