




import pickle

import numpy as np
from matplotlib import pyplot as plt




class 		__A      :

						def __init__(		self   :List[Any]    , __snake_case   :Union[str, Any]    , __snake_case   :Dict    , __snake_case   :Any    , __snake_case   :Dict    , __snake_case   :str    , __snake_case   :Optional[int]=0.2    , __snake_case   :Tuple=0.2 ):
										'''simple docstring'''

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

						def     A__						(		self   :str    , __snake_case   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[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(__snake_case    , """wb""" ) as f:
														pickle.dump(__snake_case    , __snake_case )

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

						@classmethod
						def     A__						(		cls   :str    , __snake_case   :List[str] ):
										'''simple docstring'''

										with open(__snake_case    , """rb""" ) as f:
														__magic_name__	:	Optional[int]								=pickle.load(__snake_case )  # noqa: S301

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

						def     A__						(		self   :Optional[int]    , __snake_case   :int ):
										'''simple docstring'''

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

						def     A__						(		self   :Union[str, Any]    , __snake_case   :Any ):
										'''simple docstring'''

										return round(__snake_case    , 3 )

						def     A__						(		self   :str    , __snake_case   :Optional[int]    , __snake_case   :Dict    , __snake_case   :str    , __snake_case   :Dict    , __snake_case   :str ):
										'''simple docstring'''

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

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

						def     A__						(		self   :Dict    , __snake_case   :List[str]    , __snake_case   :int    , __snake_case   :Optional[int]="average_pool" ):
										'''simple docstring'''

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

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Tuple    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	int								=np.asarray(__snake_case )
										__magic_name__	:	Optional[int]								=np.shape(__snake_case )
										__magic_name__	:	List[str]								=data_mat.reshape(1    , shapes[0] * shapes[1] )
										return data_expanded

						def     A__						(		self   :Optional[Any]    , __snake_case   :Any    , __snake_case   :int    , __snake_case   :int    , __snake_case   :Dict    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Any								=[]
										__magic_name__	:	Dict								=0
										for i_map in range(__snake_case ):
														__magic_name__	:	Tuple								=np.ones((size_map, size_map) )
														for i in range(0    , __snake_case    , __snake_case ):
																		for j in range(0    , __snake_case    , __snake_case ):
																						__magic_name__	:	str								=pd_pool[
																						    i_pool
																						]
																						__magic_name__	:	Any								=i_pool + 1
														__magic_name__	:	Union[str, Any]								=np.multiply(
														    __snake_case    , np.multiply(out_map[i_map]    , (1 - out_map[i_map]) ) )
														pd_all.append(__snake_case )
										return pd_all

						def     A__						(		self   :Dict    , __snake_case   :Dict    , __snake_case   :Tuple    , __snake_case   :Union[str, Any]    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[Any]    , __snake_case   :str=bool ):
										'''simple docstring'''

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

																		__magic_name__	:	str								=np.dot(__snake_case    , self.vji.T ) - self.thre_bpa
																		__magic_name__	:	List[Any]								=self.sig(__snake_case )
																		__magic_name__	:	Union[str, Any]								=np.dot(__snake_case    , self.wkj.T ) - self.thre_bpa
																		__magic_name__	:	Optional[int]								=self.sig(__snake_case )

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

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

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

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

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

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

						def     A__						(		self   :Optional[int]    , __snake_case   :int ):
										'''simple docstring'''

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

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


						def     A__						(		self   :Dict    , __snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=np.asmatrix(__snake_case )
										__magic_name__				, __magic_name__	:	Any								=self.convolute(
										    __snake_case    , self.conva    , self.w_conva    , self.thre_conva    , conv_step=self.step_conva    , )
										__magic_name__	:	Optional[Any]								=self.pooling(__snake_case    , self.size_poolinga )

										return data_conveda, data_pooleda


if __name__ == "__main__":
							pass










import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

										__magic_name__	:	List[Any]								=["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
										__magic_name__	:	int								={"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""merges_file"""] )
										with open(self.vocab_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write(json.dumps(__snake_case ) + """\n""" )
										with open(self.merges_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write("""\n""".join(__snake_case ) )

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

										__magic_name__	:	Any								=[0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case )    , __snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

										assert encoded[-1] == encoded_dot[0]




