

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_        =			{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''RemBertTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''RemBertTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RemBertForCausalLM''',
           '''RemBertForMaskedLM''',
           '''RemBertForMultipleChoice''',
           '''RemBertForQuestionAnswering''',
           '''RemBertForSequenceClassification''',
           '''RemBertForTokenClassification''',
           '''RemBertLayer''',
           '''RemBertModel''',
           '''RemBertPreTrainedModel''',
           '''load_tf_weights_in_rembert''',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRemBertForCausalLM''',
           '''TFRemBertForMaskedLM''',
           '''TFRemBertForMultipleChoice''',
           '''TFRemBertForQuestionAnswering''',
           '''TFRemBertForSequenceClassification''',
           '''TFRemBertForTokenClassification''',
           '''TFRemBertLayer''',
           '''TFRemBertModel''',
           '''TFRemBertPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert import RemBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert_fast import RemBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rembert import (
                  REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RemBertForCausalLM,
                  RemBertForMaskedLM,
                  RemBertForMultipleChoice,
                  RemBertForQuestionAnswering,
                  RemBertForSequenceClassification,
                  RemBertForTokenClassification,
                  RemBertLayer,
                  RemBertModel,
                  RemBertPreTrainedModel,
                  load_tf_weights_in_rembert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rembert import (
                  TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRemBertForCausalLM,
                  TFRemBertForMaskedLM,
                  TFRemBertForMultipleChoice,
                  TFRemBertForQuestionAnswering,
                  TFRemBertForSequenceClassification,
                  TFRemBertForTokenClassification,
                  TFRemBertLayer,
                  TFRemBertModel,
                  TFRemBertPreTrainedModel,
              )


else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 50_000_000      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	set()
     lowercase__   :       Any				=	int((limit - 24) ** (1 / 2)      )

     lowercase__   :       Any				=	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:
          lowercase__   :       Any				=	primea * primea
          for primea in primes:
               lowercase__   :       Optional[int]				=	primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    lowercase__   :       Dict				=	primea * primea * primea * primea
                    lowercase__   :       List[Any]				=	square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(SCREAMING_SNAKE_CASE_      )

     return len(SCREAMING_SNAKE_CASE_      )


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

