






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				):
     a		:      Any		 =     DebertaTokenizer
     a		:      List[str]		 =     True
     a		:      Optional[Any]		 =     DebertaTokenizerFast







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          super().setUp()

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

          a     =				os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"]			)
          a     =				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(__lowercase			) + "\n"			)
          with open(self.merges_file  ,		"w"  ,		encoding="utf-8"			) as fp:
               fp.write("\n".join(__lowercase			)			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   List[Any]:



          '''simple docstring'''



          kwargs.update(self.special_tokens_map			)
          return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**__lowercase			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				'''lower newer'''
          a     =				'''lower newer'''
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a     =				'''lower newer'''
          a     =				['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
          a     =				tokenizer.tokenize(__lowercase			)
          self.assertListEqual(__lowercase  ,		__lowercase			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase			)  ,		__lowercase			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a     =				tokenizer("Hello"  ,		"World"			)
          a     =				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
          self.assertListEqual(tokd["token_type_ids"]  ,		__lowercase			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("microsoft/deberta-base"			)

          a     =				tokenizer.encode("sequence builders"  ,		add_special_tokens=__lowercase			)
          a     =				tokenizer.encode("multi-sequence build"  ,		add_special_tokens=__lowercase			)

          a     =				tokenizer.encode(
              "sequence builders"  ,		add_special_tokens=__lowercase  ,		add_prefix_space=__lowercase			)
          a     =				tokenizer.encode(
              "sequence builders"  ,		"multi-sequence build"  ,		add_special_tokens=__lowercase  ,		add_prefix_space=__lowercase			)

          a     =				tokenizer.build_inputs_with_special_tokens(__lowercase			)
          a     =				tokenizer.build_inputs_with_special_tokens(__lowercase  ,		__lowercase			)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				[self.tokenizer_class]
          if self.test_rust_tokenizer:
               tokenizer_classes.append(self.rust_tokenizer_class			)

          for tokenizer_class in tokenizer_classes:
               a     =				tokenizer_class.from_pretrained("microsoft/deberta-base"			)

               a     =				[
                   '''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.''',
               ]

               a     =				tokenizer(__lowercase  ,		padding=__lowercase			)
               a     =				[tokenizer.decode(__lowercase  ,		skip_special_tokens=__lowercase			) for seq in encoding['''input_ids''']]

               # fmt: off
               a     =				{
                   '''input_ids''': [
                       [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 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, 11126, 565, 24536, 80, 43797, 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, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

               a     =				[
                   '''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  ,		__lowercase			)

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







from math import isqrt

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     return all(number % divisor != 0 for divisor in range(2		, isqrt(__UpperCamelCase) + 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 10**6)      ->					int:
     a     =				0
     a     =				1
     a     =				7
     while prime_candidate < max_prime:
          primes_count += is_prime(__UpperCamelCase)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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

