



"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       int = 10**6      ):
			"""simple docstring"""

			if n <= 0:
						raise ValueError('''Please enter an integer greater than 0'''      )

			lowerCamelCase__						:						Tuple						      =list(range(n + 1      )      )
			for number in range(2				,						n + 1      ):
						if phi[number] == number:
									phi[number] -= 1
									for multiple in range(number * 2				,						n + 1				,						__lowerCamelCase      ):
												lowerCamelCase__						:						Union[str, Any]						      =(phi[multiple] // number) * (number - 1)

			lowerCamelCase__						:						Union[str, Any]						      =1
			for number in range(1				,						n + 1      ):
						if (answer / phi[answer]) < (number / phi[number]):
									lowerCamelCase__						:						Union[str, Any]						      =number

			return answer


if __name__ == "__main__":
		print(solution())







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




