'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt





class 					__magic_name__		:







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]					):
										# save model dict with pickle
										lowercase_      :    int       		= {
										    """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(lowercase_       ,    """wb"""					) as f:
																	pickle.dump(lowercase_       ,    lowercase_					)

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







			@classmethod
			def 			SCREAMING_SNAKE_CASE_  (			cls       :						Any       ,    lowercase_       :						List[Any]					):
										# read saved model
										with open(lowercase_       ,    """rb"""					) as f:
																	lowercase_      :    List[Any]       		= pickle.load(lowercase_					)  # noqa: S301

										lowercase_      :    Any       		= model_dic.get("""conv1"""					)
										conv_get.append(model_dic.get("""step_conv1"""					)					)
										lowercase_      :    Tuple       		= model_dic.get("""size_pooling1"""					)
										lowercase_      :    Tuple       		= model_dic.get("""num_bp1"""					)
										lowercase_      :    Dict       		= model_dic.get("""num_bp2"""					)
										lowercase_      :    Tuple       		= model_dic.get("""num_bp3"""					)
										lowercase_      :    List[str]       		= model_dic.get("""rate_weight"""					)
										lowercase_      :    Optional[int]       		= model_dic.get("""rate_thre"""					)
										# create model instance
										lowercase_      :    List[str]       		= CNN(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
										# modify model parameter
										lowercase_      :    int       		= model_dic.get("""w_conv1"""					)
										lowercase_      :    str       		= model_dic.get("""wkj"""					)
										lowercase_      :    List[Any]       		= model_dic.get("""vji"""					)
										lowercase_      :    Union[str, Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										return 1 / (1 + np.exp(-1 * x					))







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]					):
										return round(lowercase_       ,    3					)







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

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







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







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Dict					):
										# expanding matrix to one dimension list
										lowercase_      :    Optional[int]       		= np.asarray(lowercase_					)
										lowercase_      :    Optional[Any]       		= np.shape(lowercase_					)
										lowercase_      :    Tuple       		= data_mat.reshape(1       ,    shapes[0] * shapes[1]					)
										return data_expanded







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Tuple       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						Any=bool					):
										# model traning
										print("""----------------------Start Training-------------------------"""					)
										print((""" - - Shape: Train_Data  """, np.shape(lowercase_					))					)
										print((""" - - Shape: Teach_Data  """, np.shape(lowercase_					))					)
										lowercase_      :    Tuple       		= 0
										lowercase_      :    Optional[Any]       		= []
										lowercase_      :    str       		= 10000
										while rp < n_repeat and mse >= error_accuracy:
																	lowercase_      :    Any       		= 0
																	print(f'''-------------Learning Time {rp}--------------'''					)
																	for p in range(len(lowercase_					)					):
																								# print('------------Learning Image: %d--------------'%p)
																								lowercase_      :    Any       		= np.asmatrix(datas_train[p]					)
																								lowercase_      :    Optional[int]       		= np.asarray(datas_teach[p]					)
																								lowercase_      :    List[Any]       		= self.convolute(
																								    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
																								lowercase_      :    List[Any]       		= self.pooling(lowercase_       ,    self.size_poolinga					)
																								lowercase_      :    List[str]       		= np.shape(lowercase_					)
																								lowercase_      :    Dict       		= self._expand(lowercase_					)
																								lowercase_      :    Tuple       		= data_bp_input

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

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

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

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

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

										def draw_error():
																	lowercase_      :    str       		= [error_accuracy for i in range(int(n_repeat * 1.2					)					)]
																	plt.plot(lowercase_       ,    """+-"""					)
																	plt.plot(lowercase_       ,    """r--"""					)
																	plt.xlabel("""Learning Times"""					)
																	plt.ylabel("""All_mse"""					)
																	plt.grid(lowercase_       ,    alpha=0.5					)
																	plt.show()

										print("""------------------Training Complished---------------------"""					)
										print((""" - - Training epoch: """, rp, f'''     - - Mse: {mse:.6f}''')					)
										if draw_e:
																	draw_error()
										return mse







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						List[str]					):
										# model predict
										lowercase_      :    List[Any]       		= []
										print("""-------------------Start Testing-------------------------"""					)
										print((""" - - Shape: Test_Data  """, np.shape(lowercase_					))					)
										for p in range(len(lowercase_					)					):
																	lowercase_      :    Any       		= np.asmatrix(datas_test[p]					)
																	lowercase_      :    Any       		= self.convolute(
																	    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
																	lowercase_      :    List[str]       		= self.pooling(lowercase_       ,    self.size_poolinga					)
																	lowercase_      :    List[Any]       		= self._expand(lowercase_					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										# return the data of image after convoluting process so we can check it out
										lowercase_      :    str       		= np.asmatrix(lowercase_					)
										lowercase_      :    Optional[int]       		= self.convolute(
										    lowercase_       ,    self.conva       ,    self.w_conva       ,    self.thre_conva       ,    conv_step=self.step_conva       ,    )
										lowercase_      :    Dict       		= self.pooling(lowercase_       ,    self.size_poolinga					)

										return data_conveda, data_pooleda


if __name__ == "__main__":
			pass






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





