


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		DebertaTokenizer
				SCREAMING_SNAKE_CASE__				:			Union[str, Any]						=		True
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		DebertaTokenizerFast


				def 		__magic_name__(       self			:List[str]	)		->			str:
										super().setUp()

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

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


				def 		__magic_name__(       self			:Any			,			**lowerCAmelCase__			:str	)		->			Union[str, Any]:
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:int	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''lower newer'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''lower newer'''
										return input_text, output_text


				def 		__magic_name__(       self			:str	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     Tuple					=		'''lower newer'''
										__SCREAMING_SNAKE_CASE						:     str					=		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer.tokenize(lowerCAmelCase__	)
										self.assertListEqual(lowerCAmelCase__			,			lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tokenizer('''Hello'''			,			'''World'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']			,			lowerCAmelCase__	)


				@slow
				def 		__magic_name__(       self			:Optional[int]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''	)

										__SCREAMING_SNAKE_CASE						:     int					=		tokenizer.encode('''sequence builders'''			,			add_special_tokens=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.encode('''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer.encode(
										    '''sequence builders'''			,			add_special_tokens=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer.encode(
										    '''sequence builders'''			,			'''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Any					=		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__			,			lowerCAmelCase__	)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode



				@slow
				def 		__magic_name__(       self			:Any	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		[self.tokenizer_class]
										if self.test_rust_tokenizer:
																tokenizer_classes.append(self.rust_tokenizer_class	)

										for tokenizer_class in tokenizer_classes:
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer_class.from_pretrained('''microsoft/deberta-base'''	)

																__SCREAMING_SNAKE_CASE						:     Dict					=		[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(lowerCAmelCase__			,			padding=lowerCAmelCase__	)
																__SCREAMING_SNAKE_CASE						:     Tuple					=		[tokenizer.decode(lowerCAmelCase__			,			skip_special_tokens=lowerCAmelCase__	) for seq in encoding['''input_ids''']]

																# fmt: off
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		{
																    '''input_ids''': [
																        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
																    ],
																    '''token_type_ids''': [
																        [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, 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]
																    ],
																    '''attention_mask''': [
																        [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],
																        [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],
																        [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]
																    ]
																}
																# fmt: on

																__SCREAMING_SNAKE_CASE						:     List[str]					=		[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																self.assertDictEqual(encoding.data			,			lowerCAmelCase__	)

																for expected, decoded in zip(lowerCAmelCase__			,			lowerCAmelCase__	):
																						self.assertEqual(lowerCAmelCase__			,			lowerCAmelCase__	)




import sys

snake_case				:    int       				=     (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str = N						):




      """simple docstring"""





      a						:Optional[Any] 							=       -sys.maxsize - 1
      for i in range(len(UpperCAmelCase_						) - 12						):
            a						:Dict 							=       1
            for j in range(13						):
                  product *= int(n[i + j]						)
            if product > largest_product:
                  a						:str 							=       product
      return largest_product


if __name__ == "__main__":
     print(F"""{solution() = }""")





