



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case    :		Union[str, Any]  =get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =XLMRobertaTokenizer
		snake_case_     =XLMRobertaTokenizerFast
		snake_case_     =True
		snake_case_     =True



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__					:    List[Any]												=					XLMRobertaTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)
						tokenizer.save_pretrained(self.tmpdirname			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					'''<pad>'''
						lowerCAmelCase__					:    str												=					1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase			)		,__lowerCamelCase			)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase			)		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					list(self.get_tokenizer().get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]		,'''<s>'''			)
						self.assertEqual(vocab_keys[1]		,'''<pad>'''			)
						self.assertEqual(vocab_keys[-1]		,'''<mask>'''			)
						self.assertEqual(len(__lowerCamelCase			)		,10_02			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size		,10_02			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					XLMRobertaTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					tokenizer.tokenize('''This is a test'''			)
						self.assertListEqual(__lowerCamelCase		,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)		,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,)

						lowerCAmelCase__					:    List[Any]												=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''9''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''é''',
						        '''.''',
						    ]		,)
						lowerCAmelCase__					:    List[str]												=					tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
						    ]		,)

						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''<unk>''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''<unk>''',
						        '''.''',
						    ]		,)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						if not self.test_slow_tokenizer:
										# as we don't have a slow version, we can't compare the outputs between slow and fast versions
										return

						lowerCAmelCase__					:    Optional[int]												=					(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
														lowerCAmelCase__					:    Tuple												=					self.rust_tokenizer_class.from_pretrained(__lowerCamelCase		,**__lowerCamelCase			)
														lowerCAmelCase__					:    Optional[int]												=					self.tokenizer_class.from_pretrained(__lowerCamelCase		,**__lowerCamelCase			)

														lowerCAmelCase__					:    Dict												=					tempfile.mkdtemp()

														lowerCAmelCase__					:    Dict												=					tokenizer_r.save_pretrained(__lowerCamelCase			)
														lowerCAmelCase__					:    List[str]												=					tokenizer_p.save_pretrained(__lowerCamelCase			)

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files			)			)
														lowerCAmelCase__					:    Dict												=					tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f			)
														self.assertSequenceEqual(__lowerCamelCase		,__lowerCamelCase			)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__					:    List[str]												=					tokenizer_r.from_pretrained(__lowerCamelCase			)
														lowerCAmelCase__					:    Dict												=					tokenizer_p.from_pretrained(__lowerCamelCase			)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase		,__lowerCamelCase			)			)
																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(__lowerCamelCase			)

														# Save tokenizer rust, legacy_format=True
														lowerCAmelCase__					:    str												=					tempfile.mkdtemp()

														lowerCAmelCase__					:    Optional[int]												=					tokenizer_r.save_pretrained(__lowerCamelCase		,legacy_format=__lowerCamelCase			)
														lowerCAmelCase__					:    Tuple												=					tokenizer_p.save_pretrained(__lowerCamelCase			)

														# Checks it save with the same files
														self.assertSequenceEqual(__lowerCamelCase		,__lowerCamelCase			)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__					:    Dict												=					tokenizer_r.from_pretrained(__lowerCamelCase			)
														lowerCAmelCase__					:    int												=					tokenizer_p.from_pretrained(__lowerCamelCase			)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase		,__lowerCamelCase			)			)

														shutil.rmtree(__lowerCamelCase			)

														# Save tokenizer rust, legacy_format=False
														lowerCAmelCase__					:    Optional[int]												=					tempfile.mkdtemp()

														lowerCAmelCase__					:    Dict												=					tokenizer_r.save_pretrained(__lowerCamelCase		,legacy_format=__lowerCamelCase			)
														lowerCAmelCase__					:    Optional[int]												=					tokenizer_p.save_pretrained(__lowerCamelCase			)

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files			)			)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__					:    int												=					tokenizer_r.from_pretrained(__lowerCamelCase			)
														lowerCAmelCase__					:    Dict												=					tokenizer_p.from_pretrained(__lowerCamelCase			)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase		,__lowerCamelCase			)			)

														shutil.rmtree(__lowerCamelCase			)



		@cached_property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						with tempfile.NamedTemporaryFile() as f:
										shutil.copyfile(__lowerCamelCase		,f.name			)
										lowerCAmelCase__					:    Dict												=					XLMRobertaTokenizer(f.name		,keep_accents=__lowerCamelCase			)
										lowerCAmelCase__					:    Optional[Any]												=					pickle.dumps(__lowerCamelCase			)
						pickle.loads(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						if not self.test_rust_tokenizer:
										return

						lowerCAmelCase__					:    Union[str, Any]												=					self.get_tokenizer()
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_rust_tokenizer()

						lowerCAmelCase__					:    int												=					'''I was born in 92000, and this is falsé.'''

						lowerCAmelCase__					:    Union[str, Any]												=					tokenizer.tokenize(__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					rust_tokenizer.tokenize(__lowerCamelCase			)
						self.assertListEqual(__lowerCamelCase		,__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.encode(__lowerCamelCase		,add_special_tokens=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					rust_tokenizer.encode(__lowerCamelCase		,add_special_tokens=__lowerCamelCase			)
						self.assertListEqual(__lowerCamelCase		,__lowerCamelCase			)

						lowerCAmelCase__					:    Any												=					self.get_rust_tokenizer()
						lowerCAmelCase__					:    List[Any]												=					tokenizer.encode(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					rust_tokenizer.encode(__lowerCamelCase			)
						self.assertListEqual(__lowerCamelCase		,__lowerCamelCase			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					'''Hello World!'''
						lowerCAmelCase__					:    Union[str, Any]												=					[0, 3_53_78, 66_61, 38, 2]
						# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
						# xlmr.eval()
						# xlmr.encode(symbols)

						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					(
						    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
						    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
						)
						lowerCAmelCase__					:    Union[str, Any]												=					[
						    0,
						    32_93,
						    83,
						    10,
						    45_52,
						    49_89,
						    79_86,
						    6_78,
						    10,
						    59_15,
						    1_11,
						    17_94_59,
						    12_48_50,
						    4,
						    60_44,
						    2_37,
						    12,
						    6,
						    5,
						    6,
						    4,
						    67_80,
						    7_05,
						    15,
						    13_88,
						    44,
						    3_78,
						    1_01_14,
						    7_11,
						    1_52,
						    20,
						    6,
						    5,
						    2_23_76,
						    6_42,
						    12_21,
						    1_51_90,
						    3_41_53,
						    4_50,
						    56_08,
						    9_59,
						    11_19,
						    5_77_02,
						    1_36,
						    1_86,
						    47,
						    10_98,
						    2_93_67,
						    47,
						    # 4426, # What fairseq tokenizes from "<unk>": "_<"
						    # 3678, # What fairseq tokenizes from "<unk>": "unk"
						    # 2740, # What fairseq tokenizes from "<unk>": ">"
						    3,  # What we tokenize from "<unk>": "<unk>"
						    6,  # Residue from the tokenization: an extra sentencepiece underline
						    4,
						    60_44,
						    2_37,
						    62_84,
						    5_09_01,
						    5_28,
						    31,
						    90,
						    34,
						    9_27,
						    2,
						]
						# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
						# xlmr.eval()
						# xlmr.encode(symbols)

						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowerCamelCase		,model_name='''xlm-roberta-base'''		,revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''		,)






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''			)
										if not isinstance(self.gamma		,(float, int)			):
														raise ValueError('''gamma must be float or int'''			)
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''			)
										lowerCAmelCase__					:    Optional[Any]												=					self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					0
						for i in range(__lowerCamelCase			):
										for j in range(__lowerCamelCase			):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]		,observations[j]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

