


def 							lowerCAmelCase_	(   __lowerCamelCase = 6_0_0_8_5_1_4_7_5_1_4_3      ):

							try:
														__snake_case						:     Optional[Any]									=  int(__lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__snake_case						:     Dict									=  2
							__snake_case						:     List[Any]									=  0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__snake_case						:     Optional[int]									=  i
														while n % i == 0:
																					__snake_case						:     Optional[Any]									=  n // i
														i += 1
							return int(__lowerCamelCase      )


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








"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



