import pickle

import numpy as np
from matplotlib import pyplot as plt






class 					a__     :






				def __init__(    self       :						Optional[int]  ,							A_       :						int  ,							A_       :						Dict  ,							A_       :						Optional[Any]  ,							A_       :						Tuple  ,							A_       :						Optional[Any]  ,							A_       :						Optional[int]=0.2  ,							A_       :						int=0.2							)       ->      List[Any]:

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






				def   lowerCAmelCase (    self       :						Union[str, Any]  ,							A_       :						List[str]							)       ->      Union[str, Any]:

										"""simple docstring"""
										lowerCamelCase_:       Tuple					     = {
										    """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(_lowerCamelCase  ,							"""wb"""							) as f:
																pickle.dump(_lowerCamelCase  ,							_lowerCamelCase							)

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






				@classmethod
				def   lowerCAmelCase (    cls       :						List[Any]  ,							A_       :						str							)       ->      Any:

										"""simple docstring"""
										# read saved model
										with open(_lowerCamelCase  ,							"""rb"""							) as f:
																lowerCamelCase_:       Optional[Any]					     = pickle.load(_lowerCamelCase							)  # noqa: S301

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






				def   lowerCAmelCase (    self       :						int  ,							A_       :						List[str]							)       ->      Dict:

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






				def   lowerCAmelCase (    self       :						Dict  ,							A_       :						str							)       ->      Dict:

										"""simple docstring"""
										return round(_lowerCamelCase  ,							3							)






				def   lowerCAmelCase (    self       :						int  ,							A_       :						Dict  ,							A_       :						str  ,							A_       :						Any  ,							A_       :						int  ,							A_       :						List[Any]							)       ->      Tuple:

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

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






				def   lowerCAmelCase (    self       :						Dict  ,							A_       :						List[Any]  ,							A_       :						Optional[int]  ,							A_       :						Dict="average_pool"							)       ->      Union[str, Any]:

										"""simple docstring"""
										lowerCamelCase_:       Tuple					     = len(featuremaps[0]							)
										lowerCamelCase_:       Any					     = int(size_map / size_pooling							)
										lowerCamelCase_:       Optional[Any]					     = []
										for i_map in range(len(_lowerCamelCase							)							):
																lowerCamelCase_:       Tuple					     = featuremaps[i_map]
																lowerCamelCase_:       Union[str, Any]					     = []
																for i_focus in range(0  ,							_lowerCamelCase  ,							_lowerCamelCase							):
																						for j_focus in range(0  ,							_lowerCamelCase  ,							_lowerCamelCase							):
																												lowerCamelCase_:       Optional[int]					     = 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(_lowerCamelCase							)							)
																												elif pooling_type == "max_pooling":
																																		# max pooling
																																		map_pooled.append(np.max(_lowerCamelCase							)							)
																lowerCamelCase_:       Tuple					     = np.asmatrix(_lowerCamelCase							).reshape(_lowerCamelCase  ,							_lowerCamelCase							)
																featuremap_pooled.append(_lowerCamelCase							)
										return featuremap_pooled






				def   lowerCAmelCase (    self       :						int  ,							A_       :						List[str]							)       ->      str:

										"""simple docstring"""
										lowerCamelCase_:       Union[str, Any]					     = []
										for i in range(len(_lowerCamelCase							)							):
																lowerCamelCase_:       str					     = np.shape(data[i]							)
																lowerCamelCase_:       List[str]					     = data[i].reshape(1  ,							shapes[0] * shapes[1]							)
																lowerCamelCase_:       List[Any]					     = data_listed.getA().tolist()[0]
																data_expanded.extend(_lowerCamelCase							)
										lowerCamelCase_:       Any					     = np.asarray(_lowerCamelCase							)
										return data_expanded






				def   lowerCAmelCase (    self       :						int  ,							A_       :						str							)       ->      Optional[Any]:

										"""simple docstring"""
										lowerCamelCase_:       List[Any]					     = np.asarray(_lowerCamelCase							)
										lowerCamelCase_:       Any					     = np.shape(_lowerCamelCase							)
										lowerCamelCase_:       List[Any]					     = data_mat.reshape(1  ,							shapes[0] * shapes[1]							)
										return data_expanded






				def   lowerCAmelCase (    self       :						Union[str, Any]  ,							A_       :						List[Any]  ,							A_       :						int  ,							A_       :						Any  ,							A_       :						Tuple  ,							A_       :						Optional[int]							)       ->      str:

										"""simple docstring"""
										lowerCamelCase_:       List[str]					     = []
										lowerCamelCase_:       List[Any]					     = 0
										for i_map in range(_lowerCamelCase							):
																lowerCamelCase_:       str					     = np.ones((size_map, size_map)							)
																for i in range(0  ,							_lowerCamelCase  ,							_lowerCamelCase							):
																						for j in range(0  ,							_lowerCamelCase  ,							_lowerCamelCase							):
																												lowerCamelCase_:       Optional[Any]					     = pd_pool[
																												    i_pool
																												]
																												lowerCamelCase_:       Union[str, Any]					     = i_pool + 1
																lowerCamelCase_:       Any					     = np.multiply(
																    _lowerCamelCase  ,							np.multiply(out_map[i_map]  ,							(1 - out_map[i_map])							)							)
																pd_all.append(_lowerCamelCase							)
										return pd_all






				def   lowerCAmelCase (    self       :						List[str]  ,							A_       :						List[Any]  ,							A_       :						Optional[int]  ,							A_       :						Dict  ,							A_       :						Union[str, Any]  ,							A_       :						List[str]  ,							A_       :						str=bool							)       ->      Union[str, Any]:

										"""simple docstring"""
										print("""----------------------Start Training-------------------------"""							)
										print((""" - - Shape: Train_Data  """, np.shape(_lowerCamelCase							))							)
										print((""" - - Shape: Teach_Data  """, np.shape(_lowerCamelCase							))							)
										lowerCamelCase_:       Dict					     = 0
										lowerCamelCase_:       Optional[int]					     = []
										lowerCamelCase_:       List[Any]					     = 1_00_00
										while rp < n_repeat and mse >= error_accuracy:
																lowerCamelCase_:       Optional[int]					     = 0
																print(f"""-------------Learning Time {rp}--------------"""							)
																for p in range(len(_lowerCamelCase							)							):
																						# print('------------Learning Image: %d--------------'%p)
																						lowerCamelCase_:       Tuple					     = np.asmatrix(datas_train[p]							)
																						lowerCamelCase_:       Optional[Any]					     = np.asarray(datas_teach[p]							)
																						lowerCamelCase_     ,  lowerCamelCase_:       Optional[Any]					     = self.convolute(
																						    _lowerCamelCase  ,							self.conva  ,							self.w_conva  ,							self.thre_conva  ,							conv_step=self.step_conva  ,							)
																						lowerCamelCase_:       List[Any]					     = self.pooling(_lowerCamelCase  ,							self.size_poolinga							)
																						lowerCamelCase_:       Tuple					     = np.shape(_lowerCamelCase							)
																						lowerCamelCase_:       List[Any]					     = self._expand(_lowerCamelCase							)
																						lowerCamelCase_:       Optional[Any]					     = data_bp_input

																						lowerCamelCase_:       Any					     = np.dot(_lowerCamelCase  ,							self.vji.T							) - self.thre_bpa
																						lowerCamelCase_:       Any					     = self.sig(_lowerCamelCase							)
																						lowerCamelCase_:       Optional[int]					     = np.dot(_lowerCamelCase  ,							self.wkj.T							) - self.thre_bpa
																						lowerCamelCase_:       Any					     = self.sig(_lowerCamelCase							)

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

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

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

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

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

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






				def   lowerCAmelCase (    self       :						Tuple  ,							A_       :						int							)       ->      Tuple:

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

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






				def   lowerCAmelCase (    self       :						str  ,							A_       :						Dict							)       ->      Dict:

										"""simple docstring"""
										lowerCamelCase_:       int					     = np.asmatrix(_lowerCamelCase							)
										lowerCamelCase_     ,  lowerCamelCase_:       str					     = self.convolute(
										    _lowerCamelCase  ,							self.conva  ,							self.w_conva  ,							self.thre_conva  ,							conv_step=self.step_conva  ,							)
										lowerCamelCase_:       Union[str, Any]					     = self.pooling(_lowerCamelCase  ,							self.size_poolinga							)

										return data_conveda, data_pooleda


if __name__ == "__main__":
			pass






"""simple docstring"""






_lowercase							=				'''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_lowercase							=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowercase							=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





