






'''simple docstring'''







import pickle

import numpy as np
from matplotlib import pyplot as plt





class 	SCREAMING_SNAKE_CASE					:






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Optional[int]:
							# save model dict with pickle
							a_		:  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(__SCREAMING_SNAKE_CASE  ,						'''wb'''					) as f:
											pickle.dump(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

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






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							# read saved model
							with open(__SCREAMING_SNAKE_CASE  ,						'''rb'''					) as f:
											a_		:  Any											=      pickle.load(__SCREAMING_SNAKE_CASE					)  # noqa: S301

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			str:
							return 1 / (1 + np.exp(-1 * x					))






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Any					)			->			Optional[int]:
							return round(__SCREAMING_SNAKE_CASE  ,						3					)






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

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str="average_pool"					)			->			Any:
							# pooling process
							a_		:  Any											=      len(featuremaps[0]					)
							a_		:  Union[str, Any]											=      int(size_map / size_pooling					)
							a_		:  str											=      []
							for i_map in range(len(__SCREAMING_SNAKE_CASE					)					):
											a_		:  Union[str, Any]											=      featuremaps[i_map]
											a_		:  Optional[Any]											=      []
											for i_focus in range(0  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					):
															for j_focus in range(0  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					):
																			a_		:  str											=      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(__SCREAMING_SNAKE_CASE					)					)
																			elif pooling_type == "max_pooling":
																							# max pooling
																							map_pooled.append(np.max(__SCREAMING_SNAKE_CASE					)					)
											a_		:  Optional[Any]											=      np.asmatrix(__SCREAMING_SNAKE_CASE					).reshape(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											featuremap_pooled.append(__SCREAMING_SNAKE_CASE					)
							return featuremap_pooled






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Union[str, Any]:
							# expanding matrix to one dimension list
							a_		:  str											=      np.asarray(__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      np.shape(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      data_mat.reshape(1  ,						shapes[0] * shapes[1]					)
							return data_expanded






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






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

															a_		:  Optional[Any]											=      np.dot(__SCREAMING_SNAKE_CASE  ,						self.vji.T					) - self.thre_bpa
															a_		:  Any											=      self.sig(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      np.dot(__SCREAMING_SNAKE_CASE  ,						self.wkj.T					) - self.thre_bpa
															a_		:  List[str]											=      self.sig(__SCREAMING_SNAKE_CASE					)

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

															a_		:  Dict											=      pd_i_all / (self.size_poolinga * self.size_poolinga)
															a_		:  str											=      pd_conva_pooled.T.getA().tolist()
															a_		:  List[Any]											=      self._calculate_gradient_from_pool(
															    __SCREAMING_SNAKE_CASE  ,						__SCREAMING_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]					):
																			a_		:  int											=      self._expand_mat(pd_conva_all[k_conv]					)
																			a_		:  Tuple											=      self.rate_weight * np.dot(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

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

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

							def draw_error():
											a_		:  int											=      [error_accuracy for i in range(int(n_repeat * 1.2					)					)]
											plt.plot(__SCREAMING_SNAKE_CASE  ,						'''+-'''					)
											plt.plot(__SCREAMING_SNAKE_CASE  ,						'''r--'''					)
											plt.xlabel('''Learning Times'''					)
											plt.ylabel('''All_mse'''					)
											plt.grid(__SCREAMING_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 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Dict:
							# model predict
							a_		:  List[Any]											=      []
							print('''-------------------Start Testing-------------------------'''					)
							print((''' - - Shape: Test_Data  ''', np.shape(__SCREAMING_SNAKE_CASE					))					)
							for p in range(len(__SCREAMING_SNAKE_CASE					)					):
											a_		:  Tuple											=      np.asmatrix(datas_test[p]					)
											a_				,			a_		:  Optional[int]											=      self.convolute(
											    __SCREAMING_SNAKE_CASE  ,						self.conva  ,						self.w_conva  ,						self.thre_conva  ,						conv_step=self.step_conva  ,						)
											a_		:  Union[str, Any]											=      self.pooling(__SCREAMING_SNAKE_CASE  ,						self.size_poolinga					)
											a_		:  str											=      self._expand(__SCREAMING_SNAKE_CASE					)

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







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

							return data_conveda, data_pooleda


if __name__ == "__main__":
		pass












'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				LongformerTokenizer
			snake_case__  =				True
			snake_case__  =				LongformerTokenizerFast
			snake_case__  =				True






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a_		:  Tuple											=      [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							a_		:  Optional[Any]											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							a_		:  Union[str, Any]											=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							a_		:  Any											=      {'''unk_token''': '''<unk>'''}

							a_		:  Union[str, Any]											=      os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES['''vocab_file''']					)
							a_		:  Optional[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(__SCREAMING_SNAKE_CASE					) + '''\n'''					)
							with open(self.merges_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						**__SCREAMING_SNAKE_CASE      :			Any					)			->			int:
							kwargs.update(self.special_tokens_map					)
							return self.tokenizer_class.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			List[str]:
							kwargs.update(self.special_tokens_map					)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							a_		:  Union[str, Any]											=      '''lower newer'''
							a_		:  List[Any]											=      '''lower newer'''
							return input_text, output_text






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							a_		:  Optional[Any]											=      self.tokenizer_class(self.vocab_file  ,						self.merges_file  ,						**self.special_tokens_map					)
							a_		:  List[str]											=      '''lower newer'''
							a_		:  str											=      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							a_		:  Optional[int]											=      tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)  # , add_prefix_space=True)
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      tokens + [tokenizer.unk_token]
							a_		:  Any											=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[Any]:
							a_		:  Union[str, Any]											=      self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('''Hello world!'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)  ,						[0, 3_1414, 232, 328, 2]					)
							self.assertListEqual(
							    tokenizer.encode('''Hello world! cécé herlolip 418'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)  ,						[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]  ,						)






			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[Any]:
							a_		:  Dict											=      self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''					)

							a_		:  Tuple											=      tokenizer.encode('''sequence builders'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      tokenizer.encode('''multi-sequence build'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      tokenizer.encode(
							    '''sequence builders'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      tokenizer.encode(
							    '''sequence builders'''  ,						'''multi-sequence build'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			int:
							a_		:  str											=      self.get_tokenizer()

							a_		:  int											=      '''Encode this sequence.'''
							a_		:  List[str]											=      tokenizer.byte_encoder[''' '''.encode('''utf-8'''					)[0]]

							# Testing encoder arguments
							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}					)
							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							# Testing spaces after special tokens
							a_		:  Optional[Any]											=      '''<mask>'''
							tokenizer.add_special_tokens(
							    {'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					)}					)  # mask token has a left space
							a_		:  Optional[int]											=      tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      '''Encode <mask> sequence'''
							a_		:  List[str]											=      '''Encode <mask>sequence'''

							a_		:  int											=      tokenizer.encode(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      encoded.index(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      tokenizer.encode(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      encoded.index(__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Union[str, Any]:
							pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
															a_		:  Any											=      self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      '''A, <mask> AllenNLP sentence.'''
															a_		:  List[Any]											=      tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)
															a_		:  Dict											=      tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r['''token_type_ids''']					)  ,						sum(tokens_p['''token_type_ids''']					)					)

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)  ,						sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)  ,						)

															a_		:  str											=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
															a_		:  Tuple											=      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p['''input_ids''']  ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
															self.assertSequenceEqual(tokens_r['''input_ids''']  ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE  ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE  ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]  ,						repeat=2					):
											a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)

											a_		:  Union[str, Any]											=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											a_		:  str											=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['''add_prefix_space''']  ,						__SCREAMING_SNAKE_CASE					)

											self.assertEqual(post_processor_state['''add_prefix_space''']  ,						__SCREAMING_SNAKE_CASE					)
											self.assertEqual(post_processor_state['''trim_offsets''']  ,						__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
															a_		:  Dict											=      '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
															a_		:  Union[str, Any]											=      f'{text_of_1_token} {text_of_1_token}'

															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Optional[int]											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					) + 1, len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					) + 1, len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  int											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Union[str, Any]											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					), len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Tuple											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Any											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					), len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Union[str, Any]											=      f' {text}'

															# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
															#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
															# )
															# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
															# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
															# self.assertEqual(
															#     encoding.offset_mapping[1],
															#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
															# )

															a_		:  str											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(1, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					) + 1, 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  int											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					), 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Optional[int]											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					), 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)




