




'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_SCREAMING_SNAKE_CASE							:   int      		= logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class  _snake_case		(	lowerCAmelCase__							):
			lowerCAmelCase_     :      bool						       =    field(default=lowerCAmelCase__						,						metadata={"help": "Whether to use SortishSampler or not."}							)
			lowerCAmelCase_     :      bool						       =    field(
			    default=lowerCAmelCase__						,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}							)
			lowerCAmelCase_     :      Optional[int]						       =    field(
			    default=lowerCAmelCase__						,						metadata={
			        "help": (
			            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
			            "to the `max_length` value of the model configuration."
			        )
			    }						,						)
			lowerCAmelCase_     :      Optional[int]						       =    field(
			    default=lowerCAmelCase__						,						metadata={
			        "help": (
			            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
			            "to the `num_beams` value of the model configuration."
			        )
			    }						,						)
			lowerCAmelCase_     :      Optional[Union[str, Path, GenerationConfig]]						       =    field(
			    default=lowerCAmelCase__						,						metadata={
			        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
			    }						,						)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      super().to_dict()
										for k, v in d.items():
																	if isinstance(UpperCamelCase__				,							UpperCamelCase__							):
																								snake_case_						=      v.to_dict()
										return d










import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

       if n % 2 == 0:
              return n == 2

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

       # n - 1=d*(2**exp)
       lowerCamelCase   :      Optional[Any]			 =					0
       while count < prec:
              lowerCamelCase   :      str			 =					random.randint(2 ,n - 1    )
              lowerCamelCase   :      Dict			 =					bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if b != 1:
                     lowerCamelCase   :      str			 =					True
                     for _ in range(_SCREAMING_SNAKE_CASE    ):
                            if b == n - 1:
                                   lowerCamelCase   :      Tuple			 =					False
                                   break
                            lowerCamelCase   :      int			 =					b * b
                            b %= n
                     if flag:
                            return False
                     count += 1
       return True


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   abs(int(input('Enter bound : ').strip()))
       print('Here\'s the list of primes:')
       print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))






