from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Tuple		       = ["""MvpTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     List[Any]		       = [
					    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MvpForCausalLM""",
					    """MvpForConditionalGeneration""",
					    """MvpForQuestionAnswering""",
					    """MvpForSequenceClassification""",
					    """MvpModel""",
					    """MvpPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase	:     Any		       = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







def       __lowerCamelCase			(				UpperCamelCase__ = 50000000   ):







							'''simple docstring'''






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

							snake_case_           =					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     ,							UpperCamelCase__   )   )   )

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

							return len(UpperCamelCase__   )


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




