






"""simple docstring"""





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__					: Any=	"""▁"""

A__					: List[str]=	get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				List[Any] 						=BertGenerationTokenizer
   a      :				Union[str, Any] 						=False
   a      :				List[str] 						=True

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       super().setUp()

       UpperCamelCase__         =    BertGenerationTokenizer(snake_case_ ,				keep_accents=snake_case_      )
       tokenizer.save_pretrained(self.tmpdirname      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    '<s>'
       UpperCamelCase__         =    1

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    list(self.get_tokenizer().get_vocab().keys()      )

       self.assertEqual(vocab_keys[0] ,				'<unk>'      )
       self.assertEqual(vocab_keys[1] ,				'<s>'      )
       self.assertEqual(vocab_keys[-1] ,				'<pad>'      )
       self.assertEqual(len(snake_case_      ) ,				1002      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       self.assertEqual(self.get_tokenizer().vocab_size ,				1000      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    BertGenerationTokenizer(snake_case_ ,				keep_accents=snake_case_      )

       UpperCamelCase__         =    tokenizer.tokenize('This is a test'      )
       self.assertListEqual(snake_case_ ,				['▁This', '▁is', '▁a', '▁t', 'est']      )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(snake_case_      ) ,				[285, 46, 10, 170, 382] ,				)

       UpperCamelCase__         =    tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
       self.assertListEqual(
           snake_case_ ,				[
               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',
               '.',
           ] ,				)
       UpperCamelCase__         =    tokenizer.convert_tokens_to_ids(snake_case_      )
       self.assertListEqual(
           snake_case_ ,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,				)

       UpperCamelCase__         =    tokenizer.convert_ids_to_tokens(snake_case_      )
       self.assertListEqual(
           snake_case_ ,				[
               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>',
               '.',
           ] ,				)

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    'Hello World!'
       UpperCamelCase__         =    [1_8536, 2260, 101]

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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    (
           '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'
       )
       UpperCamelCase__         =    [
           871,
           419,
           358,
           946,
           991,
           2521,
           452,
           358,
           1357,
           387,
           7751,
           3536,
           112,
           985,
           456,
           126,
           865,
           938,
           5400,
           5734,
           458,
           1368,
           467,
           786,
           2462,
           5246,
           1159,
           633,
           865,
           4519,
           457,
           582,
           852,
           2557,
           427,
           916,
           508,
           405,
           3_4324,
           497,
           391,
           408,
           1_1342,
           1244,
           385,
           100,
           938,
           985,
           456,
           574,
           362,
           1_2597,
           3200,
           3129,
           1172,
       ]

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

   @require_torch
   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       UpperCamelCase__         =    list(self.big_tokenizer.get_vocab().keys()      )[:10]
       UpperCamelCase__         =    ' '.join(snake_case_      )
       UpperCamelCase__         =    self.big_tokenizer.encode_plus(snake_case_ ,				return_tensors='pt' ,				return_token_type_ids=snake_case_      )
       UpperCamelCase__         =    self.big_tokenizer.batch_encode_plus(
           [sequence + ' ' + sequence] ,				return_tensors='pt' ,				return_token_type_ids=snake_case_      )

       UpperCamelCase__         =    BertGenerationConfig()
       UpperCamelCase__         =    BertGenerationEncoder(snake_case_      )

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
           model(**snake_case_      )
           model(**snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       # fmt: off
       UpperCamelCase__         =    {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 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], [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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=snake_case_ ,				model_name='google/bert_for_seq_generation_L-24_bbc_encoder' ,				revision='c817d1fd1be2ffa69431227a1fe320544943d4db' ,				)












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00_00_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    set()
    UpperCamelCase__         =    int((limit - 24) ** (1 / 2)					)

    UpperCamelCase__         =    set(range(3 ,	prime_square_limit + 1 ,	2					)					)
    primes.add(2					)
    for p in range(3 ,	prime_square_limit + 1 ,	2					):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p ,	prime_square_limit + 1 ,	SCREAMING_SNAKE_CASE					)					)					)

    for primea in primes:
        UpperCamelCase__         =    primea * primea
        for primea in primes:
            UpperCamelCase__         =    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                UpperCamelCase__         =    primea * primea * primea * primea
                UpperCamelCase__         =    square + cube + tetr
                if total >= limit:
                    break
                ret.add(SCREAMING_SNAKE_CASE					)

    return len(SCREAMING_SNAKE_CASE					)


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




