





import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






    """simple docstring"""


    if n < 2:
        return False

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

    # this means n is odd
    snake_case_						:   int           =  n - 1
    snake_case_						:   Union[str, Any]           =  0
    while d % 2 == 0:
        d /= 2
        exp += 1

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


if __name__ == "__main__":
 lowerCAmelCase_    =    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)))







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Any	      =				'''biogpt'''






			def __init__(self ,							__magic_name__=4_2384 ,							__magic_name__=1024 ,							__magic_name__=24 ,							__magic_name__=16 ,							__magic_name__=4096 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=1024 ,							__magic_name__=0.02 ,							__magic_name__=1e-12 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=2 ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   List[str]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   List[Any]           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Optional[int]           =  layer_norm_eps
							snake_case_						:   str           =  scale_embedding
							snake_case_						:   Optional[Any]           =  use_cache
							snake_case_						:   Optional[Any]           =  layerdrop
							snake_case_						:   Optional[Any]           =  activation_dropout
							super().__init__(pad_token_id=__magic_name__ ,							bos_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							**__magic_name__   )
