




def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int = 50_00_00_00	):


      '''simple docstring'''



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

      lowerCamelCase_							=       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      ,				lowercase	)	)	)

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

      return len(lowercase	)


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








"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





