

import pickle

import numpy as np
from matplotlib import pyplot as plt





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Optional[int]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]=0.2							,   _UpperCamelCase :				Optional[Any]=0.2							)      ->Optional[Any]:
									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 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				Tuple							)      ->Union[str, Any]:
									# 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(lowercase_							,   '''wb'''							) as f:
															pickle.dump(lowercase_							,   lowercase_							)

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







			@classmethod
			def 				snake_case__(     cls :				List[Any]							,   _UpperCamelCase :				Tuple							)      ->Dict:
									# read saved model
									with open(lowercase_							,   '''rb'''							) as f:
															snake_case_   =	pickle.load(lowercase_							)  # 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(lowercase_							,   lowercase_							,   lowercase_							,   lowercase_							,   lowercase_							,   lowercase_							,   lowercase_							)
									# 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 				snake_case__(     self :				Any							,   _UpperCamelCase :				Optional[Any]							)      ->str:
									return 1 / (1 + np.exp(-1 * x							))







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Dict							)      ->str:
									return round(lowercase_							,   3							)







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

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







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







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Any							)      ->List[str]:
									# expanding three dimension data to one dimension list
									snake_case_   =	[]
									for i in range(len(lowercase_							)							):
															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(lowercase_							)
									snake_case_   =	np.asarray(lowercase_							)
									return data_expanded







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							)      ->Any:
									# expanding matrix to one dimension list
									snake_case_   =	np.asarray(lowercase_							)
									snake_case_   =	np.shape(lowercase_							)
									snake_case_   =	data_mat.reshape(1							,   shapes[0] * shapes[1]							)
									return data_expanded







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







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Dict=bool							)      ->int:
									# model traning
									print('''----------------------Start Training-------------------------'''							)
									print((''' - - Shape: Train_Data  ''', np.shape(lowercase_							))							)
									print((''' - - Shape: Teach_Data  ''', np.shape(lowercase_							))							)
									snake_case_   =	0
									snake_case_   =	[]
									snake_case_   =	1_0_0_0_0
									while rp < n_repeat and mse >= error_accuracy:
															snake_case_   =	0
															print(f'''-------------Learning Time {rp}--------------'''							)
															for p in range(len(lowercase_							)							):
																					# 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(
																					    lowercase_							,   self.conva							,   self.w_conva							,   self.thre_conva							,   conv_step=self.step_conva							,   )
																					snake_case_   =	self.pooling(lowercase_							,   self.size_poolinga							)
																					snake_case_   =	np.shape(lowercase_							)
																					snake_case_   =	self._expand(lowercase_							)
																					snake_case_   =	data_bp_input

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

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

																											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(lowercase_							)

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

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







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

									return data_conveda, data_pooleda


if __name__ == "__main__":
					pass





'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_									=				SqlDatasetReader(
										    '''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase,      keep_in_memory=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features''',      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],      )
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					snake_case_									=				features.copy() if features else default_expected_features
					snake_case_									=				(
					    Features({feature: Value(__UpperCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      features=__UpperCAmelCase,      cache_dir=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					with contextlib.closing(sqlitea.connect(__UpperCAmelCase	)	) as con:
										snake_case_									=				con.cursor()
										cur.execute('''SELECT * FROM dataset'''	)
										for row in cur:
															yield row


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=1	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=2	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					with pytest.raises(__UpperCAmelCase	):
										SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=0	).write()

