




"""simple docstring"""



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


SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Union[str, Any]			=	{
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Union[str, Any]           =		"""xglm"""
 _snake_case   :			str           =		["""past_key_values"""]

 _snake_case   :			List[str]           =		{
     """num_attention_heads""": """attention_heads""",
     """hidden_size""": """d_model""",
     """num_hidden_layers""": """num_layers""",
 }


 def __init__(				self  ,						lowerCamelCase=256008  ,						lowerCamelCase=2048  ,						lowerCamelCase=1024  ,						lowerCamelCase=4096  ,						lowerCamelCase=24  ,						lowerCamelCase=16  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.02  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=2  ,						lowerCamelCase=1  ,						lowerCamelCase=0  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        __a     				=  vocab_size
        __a     				=  max_position_embeddings
        __a     				=  d_model
        __a     				=  ffn_dim
        __a     				=  num_layers
        __a     				=  attention_heads
        __a     				=  activation_function
        __a     				=  dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  layerdrop
        __a     				=  init_std
        __a     				=  scale_embedding  # scale factor will be sqrt(d_model) if True
        __a     				=  use_cache

        super().__init__(
            pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase  ,						decoder_start_token_id=lowerCamelCase  ,						**lowerCamelCase  ,						)

"""simple docstring"""



import math



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  2
       __a     				=  int(math.sqrt(a							)							)  # Size of every segment
       __a     				=  [True] * (end + 1)
       __a     				=  []

       while start <= end:
              if temp[start] is True:
                     in_prime.append(a							)
                     for i in range(start * start ,  end + 1 ,  a							):
                            __a     				=  False
              start += 1
       prime += in_prime

       __a     				=  end + 1
       __a     				=  min(2 * end ,  a							)

       while low <= n:
              __a     				=  [True] * (high - low + 1)
              for each in in_prime:
                     __a     				=  math.floor(low / each							) * each
                     if t < low:
                            t += each

                     for j in range(a ,  high + 1 ,  a							):
                            __a     				=  False

              for j in range(len(a							)							):
                     if temp[j] is True:
                            prime.append(j + low							)

              __a     				=  high + 1
              __a     				=  min(high + end ,  a							)

       return prime




print(sieve(10**6))
