'''simple docstring'''






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


_UpperCAmelCase    :     Optional[int]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "transfo-xl"
   __lowerCAmelCase         =   ["mems"]
   __lowerCAmelCase         =   {
       "n_token": "vocab_size",
       "hidden_size": "d_model",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }
   def __init__(      self      :			int, UpperCamelCase__      :			Any=26_77_35, UpperCamelCase__      :			Optional[int]=[2_00_00, 4_00_00, 20_00_00], UpperCamelCase__      :			int=10_24, UpperCamelCase__      :			Union[str, Any]=10_24, UpperCamelCase__      :			List[str]=16, UpperCamelCase__      :			Any=64, UpperCamelCase__      :			Union[str, Any]=40_96, UpperCamelCase__      :			Optional[int]=4, UpperCamelCase__      :			Any=False, UpperCamelCase__      :			Dict=18, UpperCamelCase__      :			str=16_00, UpperCamelCase__      :			Optional[int]=10_00, UpperCamelCase__      :			List[str]=True, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Optional[int]=0, UpperCamelCase__      :			Any=-1, UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			str=0.0, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Dict="normal", UpperCamelCase__      :			List[str]=0.01, UpperCamelCase__      :			str=0.01, UpperCamelCase__      :			Any=0.02, UpperCamelCase__      :			List[str]=1e-5, UpperCamelCase__      :			List[Any]=0, **UpperCamelCase__      :			Any, )  ->			Dict:
        _A    		=  vocab_size
        _A    		=  []
        self.cutoffs.extend(UpperCamelCase__     )
        if proj_share_all_but_first:
             _A    		=  [False] + [True] * len(self.cutoffs     )
        else:
             _A    		=  [False] + [False] * len(self.cutoffs     )
        _A    		=  d_model
        _A    		=  d_embed
        _A    		=  d_head
        _A    		=  d_inner
        _A    		=  div_val
        _A    		=  pre_lnorm
        _A    		=  n_layer
        _A    		=  n_head
        _A    		=  mem_len
        _A    		=  same_length
        _A    		=  attn_type
        _A    		=  clamp_len
        _A    		=  sample_softmax
        _A    		=  adaptive
        _A    		=  dropout
        _A    		=  dropatt
        _A    		=  untie_r
        _A    		=  init
        _A    		=  init_range
        _A    		=  proj_init_std
        _A    		=  init_std
        _A    		=  layer_norm_epsilon
        super().__init__(eos_token_id=UpperCamelCase__, **UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			int:
        # Message copied from Transformer-XL documentation
        logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'     )
        return -1

   @max_position_embeddings.setter
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			List[Any]     )  ->			Optional[int]:
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f'The model {self.model_type} is one of the few models that has no sequence length limit.'     )






"""simple docstring"""
import math




def 					a__						(						lowerCAmelCase    )	-> list[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      UpperCAmelCase__			:							Union[str, Any]							=    2
      UpperCAmelCase__			:							List[Any]							=    int(math.sqrt(lowerCAmelCase    )    )  # Size of every segment
      UpperCAmelCase__			:							Dict							=    [True] * (end + 1)
      UpperCAmelCase__			:							str							=    []

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

      UpperCAmelCase__			:							Optional[Any]							=    end + 1
      UpperCAmelCase__			:							Dict							=    min(2 * end			,	lowerCAmelCase    )

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

                  for j in range(lowerCAmelCase			,	high + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							int							=    False

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

            UpperCAmelCase__			:							Dict							=    high + 1
            UpperCAmelCase__			:							int							=    min(high + end			,	lowerCAmelCase    )

      return prime





print(sieve(10**6))

