

'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_							=					{
    """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_							=					["""MvpTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    """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_							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int = 1000000	)							->     int:
						lowercase   :       Dict								=set(range(3      ,   __magic_name__      ,   2	)	)
						primes.add(2	)
						for p in range(3      ,   __magic_name__      ,   2	):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p      ,   __magic_name__      ,   __magic_name__	)	)	)

						lowercase   :       List[Any]								=[float(__magic_name__	) for n in range(limit + 1	)]

						for p in primes:
												for n in range(__magic_name__      ,   limit + 1      ,   __magic_name__	):
																		phi[n] *= 1 - 1 / p

						return int(sum(phi[2:]	)	)


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





