


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 			A_   (			UpperCamelCase__   , unittest.TestCase       ):




      '''simple docstring'''


      __snake_case   			=  DebertaTokenizer
      __snake_case   			=  True
      __snake_case   			=  DebertaTokenizerFast




      def 	_snake_case      (							self:			str							):
       super().setUp()

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

       __lowerCamelCase						:				Tuple						   =		os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['vocab_file']							)
       __lowerCamelCase						:				Tuple						   =		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 	_snake_case      (							self:			str		, **a:			Union[str, Any]							):
       kwargs.update(self.special_tokens_map							)
       return self.tokenizer_class.from_pretrained(self.tmpdirname		, **__lowerCamelCase							)




      def 	_snake_case      (							self:			Dict		, a:			Optional[Any]							):
       __lowerCamelCase						:				Any						   =		'lower newer'
       __lowerCamelCase						:				Union[str, Any]						   =		'lower newer'
       return input_text, output_text




      def 	_snake_case      (							self:			Tuple							):
       __lowerCamelCase						:				List[Any]						   =		self.get_tokenizer()
       __lowerCamelCase						:				Tuple						   =		'lower newer'
       __lowerCamelCase						:				Optional[int]						   =		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       __lowerCamelCase						:				Any						   =		tokenizer.tokenize(__lowerCamelCase							)
       self.assertListEqual(__lowerCamelCase		, __lowerCamelCase							)

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




      def 	_snake_case      (							self:			Any							):
       __lowerCamelCase						:				int						   =		self.get_tokenizer()
       __lowerCamelCase						:				Any						   =		tokenizer('Hello'		, 'World'							)
       __lowerCamelCase						:				Union[str, Any]						   =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']		, __lowerCamelCase							)




      @slow
      def 	_snake_case      (							self:			Optional[int]							):
       __lowerCamelCase						:				Tuple						   =		self.tokenizer_class.from_pretrained('microsoft/deberta-base'							)

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

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

       __lowerCamelCase						:				Optional[Any]						   =		tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
       __lowerCamelCase						:				Tuple						   =		tokenizer.build_inputs_with_special_tokens(__lowerCamelCase		, __lowerCamelCase							)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




      @slow
      def 	_snake_case      (							self:			str							):
       __lowerCamelCase						:				Dict						   =		[self.tokenizer_class]
       if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class							)

       for tokenizer_class in tokenizer_classes:
        __lowerCamelCase						:				Union[str, Any]						   =		tokenizer_class.from_pretrained('microsoft/deberta-base'							)

        __lowerCamelCase						:				Optional[int]						   =		[
            '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.',
        ]

        __lowerCamelCase						:				Optional[Any]						   =		tokenizer(__lowerCamelCase		, padding=__lowerCamelCase							)
        __lowerCamelCase						:				Any						   =		[tokenizer.decode(__lowerCamelCase		, skip_special_tokens=__lowerCamelCase							) for seq in encoding['input_ids']]

        # fmt: off
        __lowerCamelCase						:				Union[str, Any]						   =		{
            'input_ids': [
                [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

        __lowerCamelCase						:				Optional[Any]						   =		[
            '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 math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->list:
       A__		   =		[True] * n
       A__		   =		False
       A__		   =		False
       A__		   =		True

       for i in range(3					,	int(n**0.5 + 1    )					,	2    ):
              A__		   =		i * 2
              while index < n:
                     A__		   =		False
                     A__		   =		index + i

       A__		   =		[2]

       for i in range(3					,	UpperCamelCase__					,	2    ):
              if is_prime[i]:
                     primes.append(UpperCamelCase__    )

       return primes


def       UpperCamelCase__(			UpperCamelCase__     :			int = 99_99_66_66_33_33    )->int:
       A__		   =		math.floor(math.sqrt(UpperCamelCase__    )    ) + 1_00
       A__		   =		prime_sieve(UpperCamelCase__    )

       A__		   =		0
       A__		   =		0
       A__		   =		primes[prime_index]

       while (last_prime**2) <= limit:
              A__		   =		primes[prime_index + 1]

              A__		   =		last_prime**2
              A__		   =		next_prime**2

              # Get numbers divisible by lps(current)
              A__		   =		lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A__		   =		upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A__		   =		0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A__		   =		next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
       print(solution())





